16 Inspiring Thanksgiving Quotes

16 Inspiring Thanksgiving Quotes These inspirational Thanksgiving quotes teach us to count our blessings. If we wish to express gratitude to our friends, family, and God for these blessings, then these Thanksgiving quotes  should be helpful there, too. Giving Thanks Here are some thoughts on being grateful: Johannes A. Gaertner: AuthorTo speak gratitude is courteous and pleasant, to enact gratitude is generous and noble, but to live gratitude is to touch Heaven. William Law: English clericWould you know who is the greatest saint in the world: It is not he who prays most or fasts most, it is not he who gives most alms or is most eminent for temperance, chastity or justice; but it is he who is always thankful to God, who wills everything that God wills, who receives everything as an instance of Gods goodness and has a heart always ready to praise God for it. Melody Beattie: American authorGratitude unlocks the fullness of life. It turns what we have into enough, and more. It turns denial into acceptance, chaos to order, confusion to clarity. It can turn a meal into a feast, a house into a home, a stranger into a friend. Gratitude makes sense of our past, brings peace for today, and creates a vision for tomorrow. Frank A. Clark: Former English soccer playerIf a fellow isnt thankful for what hes got, he isnt likely to be thankful for what hes going to get. Fred De Witt Van Amburgh: Dutch cartographer and artistNone is more impoverished than the one who has no gratitude. Gratitude is a currency that we can mint for ourselves, and spend without fear of bankruptcy. John Fitzgerald Kennedy: Late American presidentAs we express our gratitude, we must never forget that the highest appreciation is not to utter words, but to live by them. Estonian ProverbWho does not thank for little will not thank for much. Ethel Watts Mumford: American authorGod gave us our relatives; thank God we can choose our friends. Meister Eckhart; German theologianIf the only prayer you said in your whole life was, Thank you, that would suffice. Galatians 6:9Do not get tired of doing what is good. Dont get discouraged and give up, for we will reap a harvest of blessing at the appropriate time. Thomas Aquinas: Catholic priest, philosopherThanksgiving is a special virtue. But ingratitude is opposed to Thanksgiving. Therefore ingratitude is a special sin. Albert Barnes: American theologianWe can always find something to be thankful for, and there may be reasons why we ought to be thankful for even those dispensations which appear dark and frowning. Henry Ward Beecher: American clergymanThe unthankful heart ... discovers no mercies; but let the thankful heart sweep through the day and, as the magnet finds the iron, so it will find, in every hour, some heavenly blessings! William Faulkner: American novelistGratitude is a quality similar to electricity: It must be produced and discharged and used up in order to exist at all. George Herbert: English poetThou that has given so much to me,Give one thing more- a grateful heart;Not thankful when it pleases me,As if Thy blessings had spare days;But such a heart, whose pulse may beThy praise.

Deep POV

Deep POV Deep POV Deep POV By Maeve Maddox One of the advantages of belonging to a writers group is that every member has different strengths and areas of expertise. As a result, we are continually learning from one another. For example, I learned about Deep POV (Point of View) from one of my colleagues. I was already familiar with First Person, Third Person, and Omniscient, but the term Deep POV was unfamiliar to me. Now that I know about it, I strive to achieve it, but its not an easy technique to master. Another term for Deep POV is limited Third Person. Its a technique that infuses Third Person POV with the intimacy of First Person Unlike ordinary Third Person, limited Third Person does away with dialogue tags and verbs such as see, notice, understand, feel, realize and think, which suggest telling as opposed to showing. Compare the following passages. Both are written in Third Person. Judy ran down the alley. She thought she could hear footsteps behind her. She realized now that she should have stayed on the main street. Her tight skirt and high heels were slowing her down. Judy picked up her pace. Footsteps sounded in her ears. Imagination? Maybe, but what if that spooky-looking man at the corner had followed her into the alley? Damn this tight skirt. She could hardly move her knees, let alone run. And these heels! What had possessed her to buy anything this high? Momma warned her about vanity. Writing in limited Third Person usually involves the expenditure of more words, but, if done effectively, the extra words add to the readers enjoyment by pulling him more deeply into the events narrated. Deep POV is to the writer what method acting is to the actor. It requires the writer to submerge herself in the character from whose point of view a scene is being seen. It requires a casting off of all inhibitions. The writer becomes the character. A useful exercise for the writer who prefers to write in Third Person is to write a scene in First Person, and then change all the nouns and pronouns to Third Person. For more on Deep POV, check out these links: Karen Kelley (Update: no longer active) Women on Writing Want to improve your English in five minutes a day? Get a subscription and start receiving our writing tips and exercises daily! Keep learning! Browse the Fiction Writing category, check our popular posts, or choose a related post below:The Yiddish Handbook: 40 Words You Should Know50 Idioms About Arms, Hands, and Fingers3 Types of Essays Are Models for Professional Writing Forms

London 2012 olympics Essay Example | Topics and Well Written Essays - 1750 words

London 2012 olympics - Essay Example So how are they doing so far?† In this regard, the objective of this study is to evaluate the above statement in alignment with the factors of success of the London Olympics through comparative analysis with other Olympic events held in the last two decades of so. One of the major factors that can depict the success of London Olympics 2012 is the improvement of socio-cultural aspect. It is important to note that the Olympics are among the major sporting event throughout the world that involves the participation maximum number of nations. It also possesses maximum number of individual participants along with sporting events, further signifying its global appeal (Hunter, 2012). Hence, spectators from different nations arrive at the venue which further creates an environment of multicultural meet at the venue city of nation. Thus, this particular social gathering results in socio-cultural bonding between different groups of people. In this regard, the London Olympics has been highly successful considering the large number of spectators visited the country from different nations of the world, which further enhanced the socio-cultural bonding among different ethnic groups. Comparatively, this particular aspect has been can be regarded to be better than that of the Athens Olympics in 2004. However, a strong argument could not be presented with regard to the socio-cultural benefits during the Sydney Olympics in 2000 and Beijing Olympics in 2008 when compared to the London Olympics (Hunter, 2012; Kuper & Sterken, 2012). Political gains of conducting any mega sporting events like Olympics, is regarded among the major success factor for the host nations. This is evident from the history through analysis of past Olympic events. The reason behind this is that in any Olympic events maximum number of nations meet or gather in common platform i.e. host nation. Thus, along with the

Why is the abortion issue back again Essay Example | Topics and Well Written Essays - 750 words - 1

Why is the abortion issue back again - Essay Example Republicans traditionally oppose abortion whereas Democrats support it. This paper analyses the reasons why Republicans brought back the abortion issue again at present. The major reason for bringing abortion issue back in the public debate is the forthcoming presidential election. Republicans clearly know that they need a serious issue to counter the increasing popularity of President Obama. It is believed that the popularity of Obama, which was once on the decline, started to grow again after the killing of Osama. Obama is going to contest the presidential elections for the second consecutive time and ordinary election issues may not reduce the public support enjoying at present by Obama. The above awareness forced the Republicans to bring back the old abortion issue once again to attack the democrats. In fact Republicans are adopting a dual standard in their policies. â€Å"If the Republicans had wanted to prevent abortions, they would have funded a thorough and mandatory sex education initiative from the earliest grades in all schools and combined it with the distribution of free contraceptives in all high schools, public and private† (Sc haeffer). President Obama signed an executive order on March 24, 2010 which forced the Republicans to raise the abortion issue again. According to this executive order or Patient Protection and Affordable Care Act; â€Å"it is necessary to establish an adequate enforcement mechanism to ensure that Federal funds are not used for abortion services (except in cases of rape or incest, or when the life of the woman would be endangered)† (Montanaro). American government is currently reducing its involvement in many of the public utility services like, healthcare, transportation, telecommunications, drinking water projects etc. In healthcare sector, government spending has been reduced considerably over the past few

The Proliferation of Corruption through Transnational Crime Essay Example for Free

The Proliferation of Corruption through Transnational Crime Essay The international struggle against terrorism has caused policy analysts globally to review the repercussions of such policies on the preponderance of corruption. Corruption has been acknowledged as a perennial, global phenomenon; however, it is only in contemporary times that the gravity of its impact on peace and security have been scrutinized (Thachuk, 2005). Transnational organized crime groups have effectively corrupted those in authority to â€Å"selling their sovereignty so as to create states of convenience from which to conduct international operations.† Thus, in carrying this our, they have caused grave disturbance of financial market movements, destructions of aspiring democratic governments and have engaged in callous practices just to achieve their villainous ends (Beare, 1997). The Transparency International Newsletter has stated that these global perpetrators have established implicit agreements with corrupt government officials who virtually steal from their own citizens. The vastness and profundity of the corruption of transnational crime groups ceases to just being bothersome for commerce; nor is it a simple mechanism for fast tracking requests from government authority, or of being a financier for elections. This global problem represents a significant threat to state sovereignty, destroying both local and global transactions. Of more crucial importance is the fact that official corruption of transnational crime groups â€Å"threaten international stability and security with relative impunity† (Transparency International Newsletter, 2001 in Thachuk, 2005). Acknowledging the grave nature of the use of official corruption, this paper proceeds with the following objectives: 1) In the context of the relationship between officialdom and transnational organized crime groups what is a satisfactory definition of official corruption that suits the purposes of research into what corrupt practices actually facilitate the activities of such groups; 2) identify which branches or units of government are most susceptible to corruption in this context (for example: police, customs, immigration departments) and why; and 3) through an extensive review of related literature, cite concrete examples from a global scale of the corrupt practices that facilitate the activities of organized crime groups. International Response to Organized Crime and Corruption International operations such as the Financial Action Task Force (FATF) have provided a means for scrutinizing various aspects of organized crime regulation through the study of specific factors in organized crime operations like money laundering. By ensuring that such processes are carried out while still respecting the sovereignty of involved countries, the knowledge regarding organized crime has immensely benefited (Beare, 1997). At present, most international efforts against organized crime have highlighted the importance of examining organized crime with respect to the location of its operations. Illegal markets initiate the proliferation of criminal activities. These are influenced by the presence of risk factors as well as the level of demand. The current approach being utilized is a joint international effort, instead of the traditional ranking systems (Beare, 1997; Thachuk, 2005). This collaborative method which is a recent development differs widely from the other approaches being utilized for the reduction of corruption. Similar to the 1960’s view on organized crime, the dominant outlook is that corruption is a threat located in foreign shores which can be remedied by identifying those nations which are the top violators (Transparency International Global Report, 2001). With such a set-up, people may fail to see that some corrupt and legitimate practices may be inextricably intertwined. Also the nature of corruption from this viewpoint would be explained without the needed historical, political and social contexts. Lastly, such an approach works on an assumption that there is a uniform definition of corruption, which may not be the same across boundaries (Transparency International Global Report, 2001). â€Å"Corruption† is currently being viewed as if its definition was the same across nations. In a similar sense, â€Å"organized crime† had previously been used as an umbrella term for all types of criminal activities. However, the issue of corruption across different jurisdictions would inevitably involve encountering different definitions and other factors. Although the usual analyses of corruption would readily acknowledge historical factors behind the development of corrupt behavior, they would still fail in recognizing that the definition of corruption varies from place to place. Some forms of behavior may be more widely accepted as corrupt when compared to others depending on the locality. Four arbitrary categories from a western perspective may be used to demonstrate the wide scope of this concept (Beare, 1997): Bribes/kick-backs. Payments are demanded or expected in return for being allowed to do legitimate business. The payment becomes the license to do business. Those who make the payments are allowed to compete or to win contracts; Election/Campaign Corruption. Illegal payments are made at the time of elections to secure continuing influence; Protection. Officials accept payments (or privilege) from criminal organizations in exchange for permitted them to engage in illegitimate businesses; Systemic top-down corruption. A nations wealth is systematically syphoned off or exploited by the ruling elites (Beare, 1997, p. 157). The aforementioned categories are neither complete nor exclusionary. They are only listed for the purpose of comparing the differences between the ill effects arising from each. Bribes or Kick-backs Bribes or kick-backs are the small or large payments which are made to facilitate the acquisition of permits, licenses and contracts. Businesses which are legal participate in this transaction in order to hasten processes or to gain an advantage over other competitors. According to Ernesto Savona (1995), being â€Å"sly† (furbo) in this case utilizing bribery would mean that a person is simply taking advantage of opportunities. However, this should still be within acceptable limits, as those charged and eventually convicted with corruption in Italy had exceeded the acceptable boundaries (Savona, 1995). There seems to be a culture of acceptance in some areas such as Latin America, where individuals in power are still expected to patronize and support people with whom they have relationships with such as their family and party (Savona, 1995). This can be seen with police demand â€Å"bites† (mordidas) instead of issuing tickets for violations, undisclosed donations being accepted by political parties, bidding for out-of-court â€Å"settlements† and the use of â€Å"speed money† to steer clear of bureaucracy (Savona, 1995). Although a uniform definition is yet to be agreed upon for this form of corruption, it nevertheless receives the most media attention. The past few years have seen how countries are openly ranked in this form of corruption based on their â€Å"reputation† in engaging in this sort of act. One organization engaging in the elimination of corruption in business practices is Transparency International (TI) (e.g. Hong Kong Independent Commission Against Corruption sponsored 1983 in Wash. DC., 1985 NY City, 1987 Hong Kong, 1989 Sydney Australia, 1992 Amsterdam, the Netherlands, 1993 Cancun Mexico, 1995 Beijing, China). This organization had sponsored several surveys which rated the â€Å"perceived level† of corruption in different countries. The 1995 TI Corruption Index tried to assess just how much corruption has affected businesses. In a similar study, Huberts (1996) interviewed delegates from different countries regarding public corruption, service and ethics. He goes on to say how his study is not well received among academicians, but aside from the sensitive nature of the data, this reception of the study may simply be due to the wide range or types of corruption. These surveys are limited to the fact that they may very well just be measuring corruption in its most blatant forms (Criminal Justice International, 1996; Companies and their Consciences, 1996). Election Corruption Providing the needed funding as well as â€Å"other forms† of support during the election period is part of this form of corruption. This is accomplished to obtain needed â€Å"influence†.   Even with international observers, many voters in countries such as Thailand and India continue to expect that their votes would be bought. Corruption continues to be an issue even after elections as the heads of state of Venezuela, Brazil, Spain and Italy serve as specific cases (Beare, 1997; Wright, 1997). In the United States, the issue of campaign costs which can run up to the hundreds of millions of dollars for candidates cannot be overlooked. MacArthur (1997) did not want to place the blame solely on the backers and businessmen alone. He cited how politicians would tend to sell different items such including an intangible commodity called â€Å"access†, which is occasionally translate to a vote for a certain bill (MacArthur, 1997). The public was exposed to stories which involved White House bedrooms being rented out for sleepovers and of Clinton being associated with Indonesian campaign funds. The purchasing of â€Å"access† and influence in political Action Committees was also readily seen. MacArthur (1997) continues to say that a candidate clamoring for reforms backed by three billionaires is no more bought than a party hack that has spent his career in obtaining money from millionaires in several occasions. Sometimes, cultural factors may affect how society would react to this form of corruption. Savona (1995) cited how a focus on corruption enabled Italy to discredit or oust old ruling class and expedited the change towards a new political system. He continued to state his fear on how new corrupters could be produced who would be able to escape the eyes of law enforcers, having learned their lesson from those who had been caught earlier. Those politicians who appear to be greatly opposed towards corruption in Latin America at times may very well have been the primary violators themselves.   The Wall Street Journal (1996) continues by citing a particular news-weekly which stated how everyone is â€Å"an accuser and accused†. Protection Another type of corruption involves allowing criminal acts to pervade in exchange for money, which is an activity aided by the presence of corrupt officials. Activities which involve the importation and exportation of goods such as drug trafficking and smuggling operations as well as illegal gambling are some probable activities (Beare, 1997). Aside from an environment provided by government which may be conducive, organized crime continues to persist because of corrupt relations existing between violators and regulatory or enforcement agencies. Through the use of violent and intimidating acts, criminal organizations may be able to influence any dispute settlement activities that are raised to control agencies. Thus, these very control agencies may be the same ones allowing the criminal operations (MacArthur, 1997). Within formerly communist and dictator-led countries, newly-granted freedoms have resulted in varying forms of social disorganization. The existence of illegal activities such as black markets under former regimes coupled with new freedoms has ensured that corruption would be rampant (MacArthur, 1997).  Ã‚  Ã‚   New laws and regulations may have the same effect as changes in government and political alignments. Approximately 800 million pounds was lost through fraudulent means in the 1996 European Union budget. Some transnational operations involve complicit government officials. Various opportunities for corruption are presented during the determination of when taxes are applicable and reimbursable. Corruption has also been traced in countries receiving aid for their transition governments or which has experienced an environmental disaster (Leiken, 1996). One case has at its center two professors from Harvard embezzling a government funds amounting to 57 million dollars for a project in Russia. One journalist stated: â€Å"The case is certain to run and run, doubtless spawning lawsuits as it goes. But for the Russians it is, at the very least, a reminder that all is not always as rosy as it sounds either in the cradle of democracy or in the stratosphere of its academia† (Harvard Caught Up in Moscow Row, 1997). Another incident in 1996 involved the arrest of a United States immigration agent by Hong Kongs Independent Commission Against Corruption. The very same agent who had cracked down on smuggling operations realized that the profits to be made were too tempting. By negotiating with various Honduran and Hong Kong officials, he was able to organize a smuggling operation for himself (Beare, 1997). Corrupt individuals may also be found in the implementation of environmental laws. Bonanno and Constance (1996) have cited how corporations, specifically those involved in the tuna-dolphin issue, are constantly in search of countries with more lenient environmental laws as well as cheaper labor and taxes. They had delved on how purse-seine tuna fishing yielded greater catches of tuna while also capturing dolphins at the same time. Prohibitions were implemented by the United States against this particular from of fishing (Bonanno Constance, 1996). Some strategies which have been employed by corporations involve changing the flags of fishing boats into those of foreign nations as well as the shipment of tuna from third-party countries. In a similar case, there is believed to be an underground garlic smuggling operation resulting from the protective tariffs on garlic in the state of California. Corrupt officials have become the beneficiaries of these illegal activities (Myers in Beare, 1997). Corruption in the ranks of police officials has also been an issue of concern for many countries such as Canada. Although these cases may be uncommon, police corruption cases tend to be highly-publicized (Myers in Beare, 1997). One possible explanation could be the development of legislation which benefits those individuals participating in money laundering investigations and sting operations. Coupled with the confiscation of huge volumes of money and drugs, the police can become especially prone to taking part in corruption (Mollen Commission Report, 1994). Different places have earned their own notorious reputations for corruption in their respective police organizations. However, the task of making approximate comparisons regarding the corruption among these different areas would prove to be seemingly impossible. This is because corruption that has not yet been caught or identified in some districts would never be accounted for (Mollen Commission Report, 1994). Systemic Corruption In 1992, an ironic sequence of events had unfolded in Brazil starting from the impeachment of then President Fernando Collor de Mello for graft and influence peddling after he had won the election through an anti-corruption platform. Following this, many of those who had accused him in Congress were also charged with embezzlement involving the committees which they had headed. A poll in 1993 had cited corruption and a weak government as the reason behind the dwindling support for democracy (Beare, 1997). This systemic category of corruption has been the most highly publicized and is characterized by the vulnerability of the whole society to different forms of corruption. At times this may even involve the illegal transfer of wealth from the country to its leadership. An illegal activity of this nature would usually be accomplished over a lengthy period of time before the voluntary exit or forced ouster of the leader. Corruption of this type is a combination of the first three types of corruption and usually implicates the elite (Beare, 1997). Countering Corruption Following the September 11, 2001 terrorism attack in New York and Washington, the chief of Interpol, Ronald Noble (2001) expressed that the struggle to combat terrorism cannot be won over through the military’s efforts alone. He further cites, [the most sophisticated security systems, the best structures, or trained and dedicated security personnel are useless, if they are undermined from the inside by a single act of corruption (Noble, 2001). The fight against transnational crime groups’ corrupt and terroristic activities is effectively staged through the democracy strongly practiced within the states they use.   While this is being said, many of the states involved are experiencing the grave repercussions of corruption by transnational crime groups. An outcome is more stringent efforts towards its resolution, including conferences, agreements and pledges made by global and regional entities collaborating to solve the issue (Noble, 2001). The Organization for Economic Cooperation and Development (OECD) and the Asian Development Bank (ADB) have established a partnership aiming to control if not totally eradicate bribery, especially among foreign officers. These parties concur that by addressing corruption as a mere crime may not adequate; it may be more practical to delve into the root causes of the problem and its role in the promotion of transnational crime.   Other entities which have pledged their commitment in combating corruption is the African Union and the Organization of American States (Introductory Proceedings ADB/OECD Conference on Combating Corruption in Asian and Pacific Economies, 1999).   An overarching strategy for the encouragement of effective governance is by implementing â€Å"structural, legal, and administrative† foolproof policy changes that will discourage corrupt activities among citizens as well as those in authority. In addition, being able to develop a country economically and strengthening its institutions will weaken the hold of transnational crime groups over these nations. Other measures to counter corruption practices is to beef up the military and security agencies of the country. For instance, the provision of financial assistance of the International Monetary Fund of the World Bank partly anchors its lending decision to the country’s initiatives to counter corruption. They have put exceptional premium on the practice of effective governance spelled out in terms of fiscal responsibility and the honesty and morality with which they undertake their transactions within the government, as attested to by the statutes stated in the IMF Code of Good Practices on Fiscal Transparency (2001). The IMF Code of Good Practices on Fiscal Transparency (2001) specifies goals for principles and practices, culled from the IMP’s prescription of good governance among its affiliate nations. Transparency International is a non-governmental organization that offers comprehensive hand-holding to those nations which aim to counter corruption and financial maneuvering of transnational crime groups (Transparency International Global Report, 2001). To carry this out, it disseminates a bulletin on â€Å"corruption-in essence using publicity and peer pressure† to encourage administrations into supporting similar programs intended to battle corruption. The group has emphasized the revelation of terrorism-related money laundering acts (Transparency International Global Report, 2001). Such a profound perspective on battling corruption is better than superficial initiatives. Numerous agreements which have been drafted since time immemorial have not made a difference in resolving this issue while transnational crime groups continue to steal â€Å"officially† from these nations (Transparency International Global Corruption Report, 2001). Instead, the most effective vehicle for countering prevalent corruption in any number of nations is the media. The â€Å"name and shame† lobbying of the media has had the most potent impact, and has made the public keenly informed about corruption. The pubilcity has emphasized the accusations wrought on the corrupt political leaders, including Guilio Andreotti, Noboru Takeshita, Alberto Fujimori, and Carlos Salinas, to mention a few (Tranparency International Global Corruption Report, 2001). More alarming is the fact that politicians use information on corrupt acitivities to launch smear campaigns against their adversaries. These have worked effectively in nations including Italy, France, Mexico, Costa Rica, Thailand, Japan, and Nigeria, among others. This strongly signifies the negativity raised among the public on corrupt officials, and those that benefit from breaching the trust of the voting public (Transparency International Global Corruption Report, 2001). Once they have been legally appointed as government officials, the honesty which they have once professed gives out and they are eaten by the corrupt system. I There have been global agreements put in place to resolve various human rights issues that have been inflicted by transnational crime groups. The metrics against   transnational crime ought to be taken in on the national, regional and global scales to attain authentic effectiveness. A handful of these initiatives have been established; for instance, in 1988 the UN Convention Against Illicit Traffic in Narcotics Drugs and Psychotropic Substances or the UN Drug Convention, it has been necessary to ask for the legal help of all nations participating in the conference (Savona Defeo, year). Another initiative at the regional level is the formation of the Financial Action Task Force at the Economic Summit of Industrialized Countries in 1989. The group aims to draft a global perspective in the resolution of money laundering. In the latter part of 1988, the Group of Ten countries established the Basel Committee on Banking Regulations and Supervisory Practices, and the Council of Europe has a draft convention on money laundering (Savona Defeo, year). Two years after in 1990, the European Plan to Fight Drugs has been instituted by the Europeran Community. The program has been further developed in 1992 (Labrouse Wallon, year). These initiatives at all levels will contribute significantly to the resolution of the corruption traced from transnational crime which have inflicted harm to global governments and their economies.The cooperation among law enforcement forces must also be encouraged to allow the sharing of information related to the movements of these crime groups.   There has been a conference among law enforcement agencies from more than 100 countries – this Naples confrence intended to share data on transnational crime. Global protocol for such information exchange must also be crafted (National legislation and its adequacy to deal with the various forms of organized transnational crime: Appropriate guidelines for legislative and other measures to be taken on the national level, 1994). However, the constraints of such initiatives are apparent at both national and global levels. For instance, in the US, they are presently very gullible to transnational crime since â€Å"federal law prohibits the CIA from sharing with the FBI intelligence that it collects abroad.† Numerous legal protections of its citizens, specifically addressing the rights of the accused, have been taken advtantaged of by high level criminals. The other loopholes between information and law enforcement are also leveraged on by these crime groups and ought to be addressed in global crime conferences (National legislation and its adequacy to deal with the various forms of organized transnational crime: Appropriate guidelines for legislative and other measures to be taken on the national level, 1994). The UN has indicated that the struggle against corruption inflicted by transnational crime groups could be imporoved if there is greater collaboration among countries in taking on laws that criminalize participation or engagement with such group activities, conspiracy, laundering and asset forfeiture. This was specified in their policy proposals for the 1994 Ministerial Conference on Organized Transnational Crime (National legislation and its adequacy to deal with the various forms of organized transnational crime: Appropriate guidelines for legislative and other measures to be taken on the national level, 1994). Moreover, these documents have also uphled the implementation of measures for addressing transnational crime (The feasibility of elaborating international instruments, including conventions, against organized transnational crime, 1994). The participation of more countries has been encouraging; however, there will remain nations whose officials have been hopelessly corrupted and whose legal systems are too backward or obsolete – precluding participation into such collaboration. There will still be loopholes in these countries, and official corruption shall surely take its toll in their law enforcement capabilities. International Response to Organized Crime and Corruption International operations such as the Financial Action Task Force (FATF) have provided a means for scrutinizing various aspects of organized crime regulation through the study of specific factors in organized crime operations like money laundering. By ensuring that such processes are carried out while still respecting the sovereignty of involved countries, the knowledge regarding organized crime has immensely benefited (Beare, 1997). At present, most international efforts against organized crime have highlighted the importance of examining organized crime with respect to the location of its operations. Illegal markets initiate the proliferation of criminal activities. These are influenced by the presence of risk factors as well as the level of demand. The current approach being utilized is a joint international effort, instead of the traditional ranking systems (Beare, 1997; Thachuk, 2005). This collaborative method which is a recent development differs widely from the other approaches being utilized for the reduction of corruption. Similar to the 1960’s view on organized crime, the dominant outlook is that corruption is a threat located in foreign shores which can be remedied by identifying those nations which are the top violators (Transparency International Global Report, 2001). With such a set-up, people may fail to see that some corrupt and legitimate practices may be inextricably intertwined. Also the nature of corruption from this viewpoint would be explained without the needed historical, political and social contexts. Lastly, such an approach works on an assumption that there is a uniform definition of corruption, which may not be the same across boundaries (Transparency International Global Report, 2001). Conclusion From the review of related literature, it may be clearly gleaned that transnational crime groups aim for states which have implicit acceptance of corruption. Upon establishment in these havens, they seek corrupt officials and security personnel who will expedite their financial dealings and routine. Among the benefits they yield from this network is access to plans of the government and eventual protection of their illegal activities (Thachuk, 2005). In cleverly setting up their operations this way, they effectively allow individuals to run governments – with the latter ceasing to be an acknowledged institution. Moreover, they disrespect the law and rid it of any legitimacy. Because of their corrupt activities, these nations are deprived of opportunities to participate in international agenda. On a more encompassing scale, the use of official corruption of transnational crime groups ceases to be a problem of the concerned republics but more of a global security issue (Beare, 1997; Thachuk, 2005). The alarmingly quick development of transnational crime is an international occurrence that merits attention. The phenomenon has successfully been integrated into political channels, corrupting them to gain legitimacy for their illegal transactions. The grave outcomes of this include the deterrment of economic growth, disrespect and threat against democracy, corruption for the law and   for some nations, ethnic violations. The loopholes posed by weakness of the states, including Africa, Latin America, and Asia make them incapable of controlling their own boundaries or to form apt internal legal groups.These borders have become webs of netting through whose holes passes the business of organized crime through corruption. The impending danger posed on countries by corruption of transnational crime groups is not caused by a single criminal entity. Instead, this is composed of a vast criminal network that has potent political and economic networks. They have effectively used corruption to deter law enforcement institutions to undertake what is just to curb criminal activity. In various countries, organized crime have substituted for the various roles of the state, serving as an obstacle to economic growth and to the development of budding democracies. Corruption as a social phenomenon and problem is not constrained by geographic borders. Each nation does have its own share of clientelism, patronage and selfish government officials. While certain nations only have a handful of common denominators in the historical, political and economic spheres, this does not imply they may not share the same corruption issues (Thachuk, 1995). While this may be the daunting case, majority of these nations have expressed their willingness to participate, share their competence, undertake training and work collaboratively with non-government organizations to solve the problem. These NGOs are hopeful that there may be mechanisms ingrained in government structure that may be utilized for detecting corrupt activities, reprimanding those involved and to attempt to foolproof the system (well nearly). In the long haul, countries are bound to feel the tension exerted on them to comply with anti-corruption measures. Democracy shall cease to be a faà §ade in promoting corrupt activities of transnational crime groups, conspiring with government officials, security and police personnel, and customs officers. Simultaneously, the pressure for governments to increase honesty and integrity in their dealings will also be focused on. The driver for these changes shall be sourced from â€Å"grassroots movements† fueled by media support. The documentation and critique of the corrupt activities of public officials is a crucial obstacle to curb immoral use of power (Thachuk, 2005). Some strategies being employed for reducing corruption may actually yield adverse results. Focus on business-related corruption is primarily motivated by Western interests which are usually detrimental to the less developed countries. An undesirable consequence arising from the labeling of nations as corrupt includes the realization of such labels through self-fulfilling prophecy. By garnering high ratings for corruption in Huberts or TI surveys, corrupt practices may actually become normalized or socially accepted in those communities (Beare, 1997). The Council of Europe utilizes a FATF-type evaluation procedure which allows member states to evaluate each other in terms of their anti-money laundering and corruption conventions. Through the meticulous review of anti-corruption legislation and policies from the different countries, corruption could then be placed in its proper context and positive results might then be achieved. However, the characteristic of corruption being deeply ingrained within the culture of different societies and thus taking various forms, may prove to be quite a hindrance (Beare, 1997). Corruption can be quite enticing for certain governments, high-ranking officials, corporations and sectors of the public. Then, it would not be advisable to think along the line that one can corruption-proof an area. On the other hand, a system should be put in place which constantly strives to create and maintain a culture of intolerance towards corruption. A possible means for combating corruption would be through changes in the personnel or other conditions such as the economic and social climate. By focusing on certain aspects of corruption, these can then be specifically identified and consequently addressed. References Beare, M. (1997). Corruption and organized crime: Lessons from history. Crime, Law Social Change, 28, 155-172. Beare, M.E. (1996). Criminal conspiracies: organized crime in canada. Toronto: Nelson Canada. Bonanno, A. Constance, D. (1996). Caught in the net: The global tuna industry. In   Environmentalism and the state. Lawrence Kansas: University of Kansas Press. Companies and their consciences. In the Economist, 1996 July 20, 15. Criminal Justice International. (1996). 1995 TI Corruption Index, 12(4), July-August. Global Corruption Report 2001. (2001). Politics and patronage: Democratic ideals compromised. Transparency International. Harvard caught up in Moscow row.(1997). The Independent, 22 May. Huberts, L. (1996). Expert views on public corruption around the globe. PSPA Publications, Department of Political Science and Public Administration, Vrije Universiteit Amsterdam Boelelaan, The Netherlands. Introductory Proceedings ADB/OECD Conference on Combating Corruption in Asian and Pacific Economies. (1999). Asian Development Bank. Labrouse, A. Wallon, A. (eds.) (1993). La Planete des drogues: organisations criminelles, guerres el blanchiment. Paris: Editions du Seuil. Leiken, R.S. (1996). Controlling the global corruption epidemic. Foreign Policy, 55-73. MacArthur, J.R. (1997). The real corrupters of the US electoral system. Globe and Mail, 21 March, Al7. Mollen Commission Report. (1994). Commission to investigate allegations of police corruption and the anti-corruption procedures of the police department, 7 July. Myers, W. (1997). Interview with Director of the Centerfor the Study of Asian Organized Crime. In Beare, M. Corruption and organized crime: Lessons from history. Crime, Law Social Change, 28, 155-172. National legislation and its adequacy to deal with the various forms of organized transnational crime: appropriate guidelines for legislative and other measures to be taken on the national level. Background document for the World Ministerial Conference on Organized Transnational Crime (Naples, 21-23 November 1994), 23. Noble, R. (2001). Interpol Press Release, Oct. 8. Savona, E. Defeo, M. (1994). Money trails: International money laundering trends and prevention/control policies, Helsinki Institute for Crime Prevention and Control (HEUNI) Report prepared for the International Conference on Preventing and Controlling Money Laundering and the Use of Proceeds of Crime: A Global Approach. Courmayeur: June. Savona, E.U. (1995). Beyond criminal law in devising anticorruption policies: lessonsfrom the italian experience. Research Group on Transnational Crime, School of Law, University of Trento, Italy. Thachuk, K. (2005). Corruption and international security. SAIS Review, 25(1), Academic Research Library, 143. Transparency International. (2001). Transparency International Newsletter. (2001). December 2001. Wright, R. (1997). Democracies in peril: Freedoms excesses reduce democracys life span. Special report to the Los Angeles Times, 17   February.

New Communication Interactivity :: Functions of Communication

The Australian newspaper was first released by Rupert Murdock on July 15, 1964. Its release instigated a change in the way news, in particular, the printed press, was communicated within Australia. By becoming a national newspaper and attempting to capture a slice of the traditional newspaper markets, The Australian was seeking to express its 'passion for change and improvement.' Http://www.theaustralian.news.com.au/sectionindex2/0,5746,About+this+paper^^TEXT,00.html (2002). The Internet has come about through the continued development of new communication technologies. The Australian saw advantages of the Internet as a way of increasing its exposure and distribution. With the creation of The Australian News web site http://www.theaustralian.news.com.au/ , a new level of interactivity between the newspaper and the reader developed. The following paragraphs will critically evaluate The Australian's web site and assess how it has extended The Australian's traditional form of communica tion being it's printed newspaper. The web site greets us with the usual mast head that we are familiar with on the front page of their news paper, but there are a number of small additions. Most noticeable is the animation within the advertising. Advertising plays a large part of any newspaper. With the eye catching stimulation brought about by movement, the ability to subconsciously avoid advertisements in newspapers is hindered within the web site. Another part of the mast head displays the words 'News Interactive'. The Australian has made attempts to interact with the reader to a greater extent in its web site, compared to the newspaper. With the newspaper you can interact by writing to the editor, responding to and submitting advertisements, filling in the crosswords, etc. The web site takes interactivity a few steps further by giving the reader greater freedom and choice to articles and options, creating an interaction between the reader (which becomes the user) and the web site (which provides the options). The newspaper on the other hand gives limited options that ultimately limit the interactivity. There are many methods or tools that the web site utilises to create an interactive environment. New conventional codes and icons that are widely accepted throughout the Internet are being used within The Australian's web site. Blue underlined text has become an accepted convention to inform the reader of a hyperlink. Web pages such as this one are constructed with hypertext, which is text, be it in the form of a sentence or just one word that contains hidden code creating links to other web pages or other hypertext.

Attendance System

Student Attendance System Based On Fingerprint Recognition and One-to-Many Matching A thesis submitted in partial ful? llment of the requirements for the degree of Bachelor of Computer Application in Computer Science by Sachin (Roll no. 107cs016) and Arun Sharma (Roll no. 107cs015) Under the guidance of : Prof. R. C. Tripathi Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela-769 008, Orissa, India 2 . Dedicated to Our Parents and Indian Scienti? c Community . 3 National Institute of Technology Rourkela Certi? cateThis is to certify that the project entitled, ‘Student Attendance System Based On Fingerprint Recognition and One-to-Many Matching’ submitted by Rishabh Mishra and Prashant Trivedi is an authentic work carried out by them under my supervision and guidance for the partial ful? llment of the requirements for the award of Bachelor of Technology Degree in Computer Science and Engineering at National Institute of Techno logy, Rourkela. To the best of my knowledge, the matter embodied in the project has not been submitted to any other University / Institute for the award of any Degree or Diploma.Date – 9/5/2011 Rourkela (Prof. B. Majhi) Dept. of Computer Science and Engineering 4 Abstract Our project aims at designing an student attendance system which could e? ectively manage attendance of students at institutes like NIT Rourkela. Attendance is marked after student identi? cation. For student identi? cation, a ? ngerprint recognition based identi? cation system is used. Fingerprints are considered to be the best and fastest method for biometric identi? cation. They are secure to use, unique for every person and does not change in one’s lifetime. Fingerprint recognition is a mature ? ld today, but still identifying individual from a set of enrolled ? ngerprints is a time taking process. It was our responsibility to improve the ? ngerprint identi? cation system for implementation on lar ge databases e. g. of an institute or a country etc. In this project, many new algorithms have been used e. g. gender estimation, key based one to many matching, removing boundary minutiae. Using these new algorithms, we have developed an identi? cation system which is faster in implementation than any other available today in the market. Although we are using this ? ngerprint identi? cation system for student identi? ation purpose in our project, the matching results are so good that it could perform very well on large databases like that of a country like India (MNIC Project). This system was implemented in Matlab10, Intel Core2Duo processor and comparison of our one to many identi? cation was done with existing identi? cation technique i. e. one to one identi? cation on same platform. Our matching technique runs in O(n+N) time as compared to the existing O(Nn2 ). The ? ngerprint identi? cation system was tested on FVC2004 and Veri? nger databases. 5 Acknowledgments We express our profound gratitude and indebtedness to Prof. B.Majhi, Department of Computer Science and Engineering, NIT, Rourkela for introducing the present topic and for their inspiring intellectual guidance, constructive criticism and valuable suggestion throughout the project work. We are also thankful to Prof. Pankaj Kumar Sa , Ms. Hunny Mehrotra and other sta? s in Department of Computer Science and Engineering for motivating us in improving the algorithms. Finally we would like to thank our parents for their support and permitting us stay for more days to complete this project. Date – 9/5/2011 Rourkela Rishabh Mishra Prashant Trivedi Contents 1 Introduction 1. 1 1. 2 1. 3 1. 4 1. 1. 6 1. 7 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . Motivation and Challenges . . . . . . . . . . . . . . . . . . . . . . . . Using Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What is ? ngerprint? . . . . . . . . . . . . . . . . . . . . . . . . . . . Why use ? ngerprints? . . . . . . . . . . . . . . . . . . . . . . . . . . . Using ? ngerprint recognition system for attendance management . . . Organization of the thesis . . . . . . . . . . . . . . . . . . . . . . . . 17 17 17 18 18 19 19 19 21 21 22 23 24 24 30 30 33 33 33 35 35 36 36 2 Attendance Management Framework 2. 2. 2 2. 3 2. 4 2. 5 Hardware – Software Level Design . . . . . . . . . . . . . . . . . . . . Attendance Management Approach . . . . . . . . . . . . . . . . . . . On-Line Attendance Report Generation . . . . . . . . . . . . . . . . . Network and Database Management . . . . . . . . . . . . . . . . . . Using wireless network instead of LAN and bringing portability . . . 2. 5. 1 2. 6 Using Portable Device . . . . . . . . . . . . . . . . . . . . . . Comparison with other student attendance systems . . . . . . . . . . 3 Fingerprint Identi? cation System 3. 1 3. 2 How Fingerprint Recognition works? . . . . . . . . . . . . . . . . . Fingerprint Identi? cation Sys tem Flowchart . . . . . . . . . . . . . . 4 Fingerprint Enhancement 4. 1 4. 2 4. 3 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Orientation estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 6 CONTENTS 4. 4 4. 5 4. 6 4. 7 Ridge Frequency Estimation . . . . . . . . . . . . . . . . . . . . . . . Gabor ? lter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Binarisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thinning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 38 39 40 40 41 41 42 42 43 44 45 45 45 46 47 47 50 51 53 53 54 54 55 56 57 59 59 59 59 60 5 Feature Extraction 5. 1 5. 2 Finding the Reference Point . . . . . . . . . . . . . . . . . . . . . . . Minutiae Extraction and Post-Processing . . . . . . . . . . . . . . . . 5. 2. 1 5. 2. 2 5. 2. 3 5. 3 Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . Post-Processing . . . . . . . . . . . . . . . . . . . . . . . . . Removing Boundary Minutiae . . . . . . . . . . . . . . . . . . Extraction of the key . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3. 1 What is key? . . . . . . . . . . . . . . . . . . . . . . . . . . Simple Key . . . . . . . . . . . . . . . . . . . . . . . . . . . . Complex Key . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Partitioning of Database 6. 1 6. 2 6. 3 Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classi? cation of Fingerprint . . . . . . . . . . . . . . . . . . . . . . . Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Matching 7. 1 7. 2 7. 3 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Existing Matching Techniques . . . . . . . . . . . . . . . . . . . . . One to Many matching . . . . . . . . . . . . . . . . . . . . . . . . . . 7. 3. 1 7. 4 7. 5 Method of One to Many Matching . . . . . . . . . . . . . . . Performing key match and full matching . . . . . . . . . . . . . . . . Time Complexity of this matching technique . . . . . . . . . . . . . . 8 Experimental Analysis 8. 1 8. 2 Implementation Environment . . . . . . . . . . . . . . . . . . . . . . Fingerprint Enhancement . . . . . . . . . . . . . . . . . . . . . . . . 8. 2. 1 8. 2. 2 Segmentation and Normalization . . . . . . . . . . . . . . . . Orientation Estimation . . . . . . . . . . . . . . . . . . . . . . 8 8. 2. 3 8. 2. 4 8. . 5 8. 3 CONTENTS Ridge Frequency Estimation . . . . . . . . . . . . . . . . . . . Gabor Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . Binarisation and Thinning . . . . . . . . . . . . . . . . . . . . 60 60 61 62 62 62 63 64 64 64 64 65 66 66 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 3. 1 Minutiae Extraction and Post Processing . . . . . . . . . . . . Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . After Removing Spuriou s and Boundary Minutiae . . . . . . . 8. 3. 2 Reference Point Detection . . . . . . . . . . . . . . . . . . . . 8. 4 Gender Estimation and Classi? ation . . . . . . . . . . . . . . . . . . 8. 4. 1 8. 4. 2 Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . Classi? cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 5 8. 6 Enrolling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 6. 1 8. 6. 2 Fingerprint Veri? cation Results . . . . . . . . . . . . . . . . . Identi? cation Results and Comparison with Other Matching techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 70 73 74 75 75 79 8. 7 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Conclusion 9. 1 Outcomes of this Project . . . . . . . . . . . . . . . . . . . . . . . . . 10 Future Work and Expectations 10. 1 Approach for Future Work A Matlab functions . . . . . . . . . . . . . . . . . . . . . . . List of Figures 1. 1 2. 1 2. 2 2. 3 2. 4 2. 5 2. 6 2. 7 2. 8 3. 1 4. 1 4. 2 Example of a ridge ending and a bifurcation . . . . . . . . . . . . . . Hardware present in classrooms . . . . . . . . . . . . . . . . . . . . . Classroom Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . Network Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ER Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 0 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 1 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 2 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Portable Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fingerprint Identi? cation System Flowchart . . . . . . . . . . . . . . Orientation Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . (a)Original Image, (b)Enhanced Image, (c)Binarised Image, (d)Thinne d Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 1 Row 1: ? lter response c1k , k = 3, 2, and 1. Row 2: ? lter response c2k , k = 3, 2, and 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 2 5. 3 Examples of (a)ridge-ending (CN=1) and (b)bifurcation pixel (CN=3) 42 43 40 18 22 23 25 26 27 27 28 29 34 37 Examples of typical false minutiae structures : (a)Spur, (b)Hole, (c)Triangle, (d)Spike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 44 44 45 48 5. 4 5. 5 5. 6 6. 1 Skeleton of window centered at boundary minutiae . . . . . . . . . . Matrix Representation of boundary minutiae . . . . . . . . . . . . . Key Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 10 6. 2 6. 3 LIST OF FIGURES 135o blocks of a ? ngerprint . . . . . . . . . . . . . . . . . . . . . . . . Fingerprint Classes (a)Left Loop, (b)Right Loop, (c)Whorl, (d 1)Arch, (d2)Tented Arch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. 4 7. 1 8. 1 8. 2 8. 3 8. 4 8. 5 8. 6 8. 7 8. 8 8. 9 Partitioning Database . . . . . . . . . . . . . . . . . . . . . . . . . . One to Many Matching . . . . . . . . . . . . . . . . . . . . . . . . . . Normalized Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . Orientation Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ridge Frequency Image . . . . . . . . . . . . . . . . . . . . . . . . . . Left-Original Image, Right-Enhanced Image . . . . . . . . . . . . . . Binarised Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thinned Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . All Extracted Minutiae . . . . . . . . . . . . . . . . . . . . . . . . . . Composite Image with spurious and boundary minutiae . . . . . . . . Minutiae Image after post-processing . . . . . . . . . . . . . . . . . 51 52 57 59 60 60 61 61 62 62 63 63 64 65 50 8. 10 Compo site Image after post-processing . . . . . . . . . . . . . . . . . 8. 11 Plotted Minutiae with Reference Point(Black Spot) . . . . . . . . . . 8. 12 Graph: Time taken for Identi? cation vs Size of Database(key based one to many identi? cation) . . . . . . . . . . . . . . . . . . . . . . . . 8. 13 Graph: Time taken for Identi? cation vs Size of Database (n2 identi? cation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 14 Expected Graph for comparison : Time taken for Identi? cation vs Size of Database(1 million) . . . . . . . . . . . . . . . . . . . . . . . . . 68 69 71 List of Tables 2. 1 5. 1 8. 1 8. 2 8. 3 8. 4 8. 5 8. 6 8. 7 8. 8 Estimated Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Properties of Crossing Number . . . . . . . . . . . . . . . . . . . . . 22 43 64 65 66 66 67 67 68 Average Number of Minutiae before and after post-processing . . . . Ridge Density Calculation Results . . . . . . . . . . . . . . . . . . . . Classi? catio n Results on Original Image . . . . . . . . . . . . . . . . Classi? cation Results on Enhanced Image . . . . . . . . . . . . . . . Time taken for Classi? cation . . . . . . . . . . . . . . . . . . . . . . .Time taken for Enrolling . . . . . . . . . . . . . . . . . . . . . . . . . Error Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Performance of ours and n2 matching based identi? cation techniques on a database of size 150 . . . . . . . . . . . . . . . . . . . . . . . . . 70 11 List of Algorithms 1 2 3 4 Key Extraction Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . Gender Estimation Algorithm . . . . . . . . . . . . . . . . . . . . . . . Key Based One to Many Matching Algorithm . . . . . . . . . . . . . . Matching Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 49 55 56 12Chapter 1 Introduction 1. 1 Problem Statement Designing a student attendance management system based on ? ngerprint recognition and faster one to many ident i? cation that manages records for attendance in institutes like NIT Rourkela. 1. 2 Motivation and Challenges Every organization whether it be an educational institution or business organization, it has to maintain a proper record of attendance of students or employees for e? ective functioning of organization. Designing a better attendance management system for students so that records be maintained with ease and accuracy was an important key behind motivating this project.This would improve accuracy of attendance records because it will remove all the hassles of roll calling and will save valuable time of the students as well as teachers. Image processing and ? ngerprint recognition are very advanced today in terms of technology. It was our responsibility to improve ? ngerprint identi? cation system. We decreased matching time by partitioning the database to one-tenth and improved matching using key based one to many matching. 13 14 CHAPTER 1. INTRODUCTION 1. 3 Using Biometrics Bi ometric Identi? cation Systems are widely used for unique identi? cation of humans mainly for veri? cation and identi? ation. Biometrics is used as a form of identity access management and access control. So use of biometrics in student attendance management system is a secure approach. There are many types of biometric systems like ? ngerprint recognition, face recognition, voice recognition, iris recognition, palm recognition etc. In this project, we used ? ngerprint recognition system. 1. 4 What is ? ngerprint? A ? ngerprint is the pattern of ridges and valleys on the surface of a ? ngertip. The endpoints and crossing points of ridges are called minutiae. It is a widely accepted assumption that the minutiae pattern of each ? ger is unique and does not change during one’s life. Ridge endings are the points where the ridge curve terminates, and bifurcations are where a ridge splits from a single path to two paths at a Y-junction. Figure 1 illustrates an example of a ridge en ding and a bifurcation. In this example, the black pixels correspond to the ridges, and the white pixels correspond to the valleys. Figure 1. 1: Example of a ridge ending and a bifurcation When human ? ngerprint experts determine if two ? ngerprints are from the same ? nger, the matching degree between two minutiae pattern is one of the most important factors.Thanks to the similarity to the way of human ? ngerprint experts and compactness of templates, the minutiae-based matching method is the most widely studied matching method. 1. 5. WHY USE FINGERPRINTS? 15 1. 5 Why use ? ngerprints? Fingerprints are considered to be the best and fastest method for biometric identi? cation. They are secure to use, unique for every person and does not change in one’s lifetime. Besides these, implementation of ? ngerprint recognition system is cheap, easy and accurate up to satis? ability. Fingerprint recognition has been widely used in both forensic and civilian applications.Compared with o ther biometrics features , ? ngerprint-based biometrics is the most proven technique and has the largest market shares . Not only it is faster than other techniques but also the energy consumption by such systems is too less. 1. 6 Using ? ngerprint recognition system for attendance management Managing attendance records of students of an institute is a tedious task. It consumes time and paper both. To make all the attendance related work automatic and on-line, we have designed an attendance management system which could be implemented in NIT Rourkela.It uses a ? ngerprint identi? cation system developed in this project. This ? ngerprint identi? cation system uses existing as well as new techniques in ? ngerprint recognition and matching. A new one to many matching algorithm for large databases has been introduced in this identi? cation system. 1. 7 Organization of the thesis This thesis has been organized into ten chapters. Chapter 1 introduces with our project. Chapter 2 explains t he proposed design of attendance management system. Chapter 3 explains the ? ngerprint identi? cation system used in this project.Chapter 4 explains enhancement techniques, Chapter 5 explains feature extraction methods, Chapter 6 explains our database partitioning approach . Chapter 7 explains matching technique. Chapter 8 explains experimental work done and performance analysis. Chapter 9 includes conclusions and Chapter 10 introduces proposed future work. Chapter 2 Attendance Management Framework Manual attendance taking and report generation has its limitations. It is well enough for 30-60 students but when it comes to taking attendance of students large in number, it is di? cult. For taking attendance for a lecture, a conference, etc. oll calling and manual attendance system is a failure. Time waste over responses of students, waste of paper etc. are the disadvantages of manual attendance system. Moreover, the attendance report is also not generated on time. Attendance report wh ich is circulated over NITR webmail is two months old. To overcome these non-optimal situations, it is necessary that we should use an automatic on-line attendance management system. So we present an implementable attendance management framework. Student attendance system framework is divided into three parts : Hardware/Software Design, Attendance Management Approach and On-line Report Generation.Each of these is explained below. 2. 1 Hardware – Software Level Design Required hardware used should be easy to maintain, implement and easily available. Proposed hardware consists following parts: (1)Fingerprint Scanner, (2)LCD/Display Module (optional), (3)Computer 16 2. 2. ATTENDANCE MANAGEMENT APPROACH Table 2. 1: Estimated Budget Device Cost of Number of Total Name One Unit Units Required Unit Budget Scanner 500 100 50000 PC 21000 100 2100000 Total 21,50,000 (4)LAN connection 17 Fingerprint scanner will be used to input ? ngerprint of teachers/students into the computer softwar e.LCD display will be displaying rolls of those whose attendance is marked. Computer Software will be interfacing ? ngerprint scanner and LCD and will be connected to the network. It will input ? ngerprint, will process it and extract features for matching. After matching, it will update database attendance records of the students. Figure 2. 1: Hardware present in classrooms Estimated Budget Estimated cost of the hardware for implementation of this system is shown in the table 2. 1. Total number of classrooms in NIT Rourkela is around 100. So number of units required will be 100. 2. 2 Attendance Management ApproachThis part explains how students and teachers will use this attendance management system. Following points will make sure that attendance is marked correctly, without any problem: (1)All the hardware will be inside classroom. So outside interference will be absent. (2)To remove unauthorized access and unwanted attempt to corrupt the hardware by students, all the hardware ex cept ? ngerprint scanner could be put inside a small 18 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK cabin. As an alternate solution, we can install CCTV cameras to prevent unprivileged activities. (3)When teacher enters the classroom, the attendance marking will start.Computer software will start the process after inputting ? ngerprint of teacher. It will ? nd the Subject ID, and Current Semester using the ID of the teacher or could be set manually on the software. If teacher doesn’t enter classroom, attendance marking will not start. (4)After some time, say 20 minutes of this process, no attendance will be given because of late entrance. This time period can be increased or decreased as per requirements. Figure 2. 2: Classroom Scenario 2. 3 On-Line Attendance Report Generation Database for attendance would be a table having following ? elds as a combination for primary ? ld: (1)Day,(2)Roll,(3)Subject and following non-primary ? elds: (1)Attendance,(2)Semester. Using this tabl e, all the attendance can be managed for a student. For on-line report generation, a simple website can be hosted on NIT Rourkela servers, 2. 4. NETWORK AND DATABASE MANAGEMENT 19 which will access this table for showing attendance of students. The sql queries will be used for report generation. Following query will give total numbers of classes held in subject CS423: SELECT COUNT(DISTINCT Day) FROM AttendanceTable WHERE SUBJECT = CS423 AND Attendance = 1 For attendance of oll 107CS016, against this subject, following query will be used: SELECT COUNT(Day) FROM AttendanceTable WHERE Roll = 107CS016 AND SUBJECT = CS423 AND Attendance = 1 Now the attendance percent can easily be calculated : ClassesAttended ? 100 ClassesHeld Attendance = (2. 1) 2. 4 Network and Database Management This attendance system will be spread over a wide network from classrooms via intranet to internet. Network diagram is shown in ? g. 2. 3. Using this network, attendance reports will be made available over in ternet and e-mail. A monthly report will be sent to each student via email and website will show the updated attendance.Entity relationship diagram for database of students and attendance records is shown in ? g. 2. 4. In ER diagram, primary ? elds are Roll, Date, SubjectID and TeacherID. Four tables are Student, Attendance, Subject and Teacher. Using this database, attendance could easily be maintained for students. Data? ow is shown in data ? ow diagrams (DFD) shown in ? gures 2. 5, 2. 6 and 2. 7. 2. 5 Using wireless network instead of LAN and bringing portability We are using LAN for communication among servers and hardwares in the classrooms. We can instead use wireless LAN with portable devices.Portable device will have an embedded ? ngerprint scanner, wireless connection, a microprocessor loaded with a software, memory and a display terminal, see ? gure 2. 5. Size of device could be small like a mobile phone depending upon how well the device is manufactured. 20 CHAPTER 2. ATT ENDANCE MANAGEMENT FRAMEWORK Figure 2. 3: Network Diagram 2. 5. USING WIRELESS NETWORK INSTEAD OF LAN AND BRINGING PORTABILITY21 Figure 2. 4: ER Diagram 22 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK Figure 2. 5: Level 0 DFD Figure 2. 6: Level 1 DFD 2. 5. USING WIRELESS NETWORK INSTEAD OF LAN AND BRINGING PORTABILITY23 Figure 2. : Level 2 DFD 24 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK This device should have a wireless connection. Using this wireless connection, Figure 2. 8: Portable Device attendance taken would be updated automatically when device is in network of the nodes which are storing the attendance records. Database of enrolled ? ngerprints will be in this portable device. Size of enrolled database was 12. 1 MB when 150 ? ngerprints were enrolled in this project. So for 10000 students, atleast 807 MB or more space would be required for storing enrolled database. For this purpose, a removable memory chip could be used.We cannot use wireless LAN here because fetching data using wireless LAN will not be possible because of less range of wireless devices. So enrolled data would be on chip itself. Attendance results will be updated when portable device will be in the range of nodes which are storing attendance reports. We may update all the records online via the mobile network provided by di? erent companies. Today 3G network provides su? cient throughput which can be used for updating attendance records automatically without going near nodes. In such case, 2. 6. COMPARISON WITH OTHER STUDENT ATTENDANCE SYSTEMS 25 he need of database inside memory chip will not be mandatory. It will be fetched by using 3G mobile network from central database repository. The design of such a portable device is the task of embedded system engineers. 2. 5. 1 Using Portable Device In this section, we suggest the working of portable device and the method of using it for marking attendance. The device may either be having touchscreen input/display or buttons with lcd display . A software specially designed for the device will be running on it. Teachers will verify his/her ? ngerprint on the device before giving it to students for marking attendance.After verifying the teacher’s identity, software will ask for course and and other required information about the class which he or she is going to teach. Software will ask teacher the time after which device will not mark any attendance. This time can vary depending on the teacher’s mood but our suggested value is 25 minutes. This is done to prevent late entrance of students. This step will hardly take few seconds. Then students will be given device for their ? ngerprint identi? cation and attendance marking. In the continuation, teacher will start his/her lecture.Students will hand over the device to other students whose attendance is not marked. After 25 minutes or the time decided by teacher, device will not input any attendance. After the class is over, teacher will take device and will end the lecture. The main function of software running on the device will be ? ngerprint identi? cation of students followed by report generation and sending reports to servers using 3G network. Other functions will be downloading and updating the database available on the device from central database repository. 2. 6 Comparison with other student attendance systemsThere are various other kind of student attendance management systems available like RFID based student attendance system and GSM-GPRS based student attendance system. These systems have their own pros and cons. Our system is better because ? rst it saves time that could be used for teaching. Second is portability. Portability 26 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK has its own advantage because the device could be taken to any class wherever it is scheduled. While GSM-GPRS based systems use position of class for attendance marking which is not dynamic and if schedule or location of the class changes, wrong attendance might be marked.Problem with RFID based systems is that students have to carry RFID cards and also the RFID detectors are needed to be installed. Nonetheless, students may give proxies easily using friend’s RFID card. These problems are not in our system. We used ? ngerprints as recognition criteria so proxies cannot be given. If portable devices are used, attendance marking will be done at any place and any time. So our student attendance system is far better to be implemented at NITR. Chapter 3 Fingerprint Identi? cation System An identi? cation system is one which helps in identifying an individual among any people when detailed information is not available. It may involve matching available features of candidate like ? ngerprints with those already enrolled in database. 3. 1 How Fingerprint Recognition works? Fingerprint images that are found or scanned are not of optimum quality. So we remove noises and enhance their quality. We extract features like minutiae and others for matching. If the sets of minutiae are matched with those in the database, we call it an identi? ed ? ngerprint. After matching, we perform post-matching steps which may include showing details of identi? ed candidate, marking attendance etc.A brief ? owchart is shown in next section. 3. 2 Fingerprint Identi? cation System Flowchart A brief methodology of our Fingerprint Identi? cation System is shown here in following ? owchart. Each of these are explained in the later chapters. 27 28 CHAPTER 3. FINGERPRINT IDENTIFICATION SYSTEM Figure 3. 1: Fingerprint Identi? cation System Flowchart Chapter 4 Fingerprint Enhancement The image acquired from scanner is sometimes not of perfect quality . It gets corrupted due to irregularities and non-uniformity in the impression taken and due to variations in the skin and the presence of the scars, humidity, irt etc. To overcome these problems , to reduce noise and enhance the de? nition of ridges against valleys, various techniques are applied as following. 4. 1 Segmentation Image segmentation [1] separates the foreground regions and the background regions in the image. The foreground regions refers to the clear ? ngerprint area which contains the ridges and valleys. This is the area of interest. The background regions refers to the regions which is outside the borders of the main ? ngerprint area, which does not contain any important or valid ? ngerprint information.The extraction of noisy and false minutiae can be done by applying minutiae extraction algorithm to the background regions of the image. Thus, segmentation is a process by which we can discard these background regions, which results in more reliable extraction of minutiae points. We are going to use a method based on variance thresholding . The background regions exhibit a very low grey – scale variance value , whereas the foreground regions have a very high variance . Firstly , the image is divided into blocks and the grey-scale variance is calculated for each block in the image .If the variance is less than the global threshold , then the block is assigned to be part of background region or else 29 30 CHAPTER 4. FINGERPRINT ENHANCEMENT it is part of foreground . The grey – level variance for a block of size S x S can be calculated as : 1 V ar(k) = 2 S S? 1 S? 1 (G(i, j) ? M (k))2 i=0 j=0 (4. 1) where Var(k) is the grey – level variance for the block k , G(i,j) is the grey – level value at pixel (i,j) , and M(k) denotes the mean grey – level value for the corresponding block k . 4. 2 Normalization Image normalization is the next step in ? ngerprint enhancement process.Normalization [1] is a process of standardizing the intensity values in an image so that these intensity values lies within a certain desired range. It can be done by adjusting the range of grey-level values in the image. Let G(i, j) denotes the grey-level value at pixel (i, j), and N(i, j) represent the normalized grey-level value at pi xel (i, j). Then the normalized image can de? ned as: ? ? M + 0 N (i, j) = ? M ? 0 V0 (G(i,j)? M )2 V V0 (G(i,j)? M )2 V , if I(i, j) > M , otherwise where M0 and V0 are the estimated mean and variance of I(i, j), respectively . 4. 3 Orientation estimation The orientation ? eld of a ? ngerprint image de? es the local orientation of the ridges contained in the ? ngerprint . The orientation estimation is a fundamental step in the enhancement process as the subsequent Gabor ? ltering stage relies on the local orientation in order to e? ectively enhance the ? ngerprint image. The least mean square estimation method used by Raymond Thai [1] is used to compute the orientation image. However, instead of estimating the orientation block-wise, we have chosen to extend their method into a pixel-wise scheme, which produces a ? ner and more accurate estimation of the orientation ? eld. The steps for calculating the orientation at pixel i, j) are as follows: 4. 3. ORIENTATION ESTIMATION 31 1. Fi rstly , a block of size W x W is centered at pixel (i, j) in the normalized ? ngerprint image. 2. For each pixel in the block, compute the gradients dx (i, j) and dy (i, j), which are the gradient magnitudes in the x and y directions, respectively. The horizontal Sobel operator[6] is used to compute dx(i, j) : [1 0 -1; 2 0 -2;1 0 -1] Figure 4. 1: Orientation Estimation 3. The local orientation at pixel (i; j) can then be estimated using the following equations: i+ W 2 j+ W 2 Vx (i, j) = u=i? W 2 i+ W 2 v=j? W 2 j+ W 2 2? x (u, v)? y (u, v) (4. 2) Vy (i, j) = u=i? W v=j? W 2 2 2 2 ? (u, v) ? ?y (u, v), (4. 3) ?(i, j) = 1 Vy (i, j) tan? 1 , 2 Vx (i, j) (4. 4) where ? (i, j) is the least square estimate of the local orientation at the block centered at pixel (i, j). 4. Smooth the orientation ? eld in a local neighborhood using a Gaussian ? lter. The orientation image is ? rstly converted into a continuous vector ? eld, which is de? ned as: ? x (i, j) = cos 2? (i, j), ? y (i, j) = sin 2 ? (i, j), (4. 5) (4. 6) where ? x and ? y are the x and y components of the vector ? eld, respectively. After 32 CHAPTER 4. FINGERPRINT ENHANCEMENT the vector ? eld has been computed, Gaussian smoothing is then performed as follows: w? w? 2 ?x (i, j) = w? u=? 2 w? v=? 2 G(u, v)? x (i ? uw, j ? vw), (4. 7) w? 2 w? 2 ?y (i, j) = w? u=? 2 w? v=? 2 G(u, v)? y (i ? uw, j ? vw), (4. 8) where G is a Gaussian low-pass ? lter of size w? x w? . 5. The ? nal smoothed orientation ? eld O at pixel (i, j) is de? ned as: O(i, j) = ? y (i, j) 1 tan? 1 2 ? x (i, j) (4. 9) 4. 4 Ridge Frequency Estimation Another important parameter,in addition to the orientation image, that can be used in the construction of the Gabor ? lter is the local ridge frequency. The local frequency of the ridges in a ? ngerprint is represented by the frequency image. The ? st step is to divide the image into blocks of size W x W. In the next step we project the greylevel values of each pixels located inside each block along a direction perpendicular to the local ridge orientation. This projection results in an almost sinusoidal-shape wave with the local minimum points denoting the ridges in the ? ngerprint. It involves smoothing the projected waveform using a Gaussian lowpass ? lter of size W x W which helps in reducing the e? ect of noise in the projection. The ridge spacing S(i, j) is then calculated by counting the median number of pixels between the consecutive minima points in the projected waveform.The ridge frequency F(i, j) for a block centered at pixel (i, j) is de? ned as: F (i, j) = 1 S(i, j) (4. 10) 4. 5. GABOR FILTER 33 4. 5 Gabor ? lter Gabor ? lters [1] are used because they have orientation-selective and frequencyselective properties. Gabor ? lters are called the mother of all other ? lters as other ? lter can be derived using this ? lter. Therefore, applying a properly tuned Gabor ? lter can preserve the ridge structures while reducing noise. An even-symmetric Gabor ? lter in the spati al domain is de? ned as : 1 x2 y2 G(x, y, ? , f ) = exp{? [ ? + ? ]} cos 2? f x? , 2 2 2 ? x ? y (4. 11) x? = x cos ? + y sin ? , (4. 12) y? ? x sin ? + y cos ? , (4. 13) where ? is the orientation of the Gabor ? lter, f is the frequency of the cosine wave, ? x and ? y are the standard deviations of the Gaussian envelope along the x and y axes, respectively, and x? and y? de? ne the x and y axes of the ? lter coordinate frame respectively. The Gabor Filter is applied to the ? ngerprint image by spatially convolving the image with the ? lter. The convolution of a pixel (i,j) in the image requires the corresponding orientation value O(i,j) and the ridge frequency value F(i,j) of that pixel . wy 2 wx 2 E(i, j) = u=? wx 2 w v=? 2y G(u, v, O(i, j), F (i, j))N (i ? u, j ? v), (4. 4) where O is the orientation image, F is the ridge frequency image, N is the normalized ? ngerprint image, and wx and wy are the width and height of the Gabor ? lter mask, respectively. 34 CHAPTER 4. FINGERPRINT ENHANCEMENT 4. 6 Binarisation Most minutiae extraction algorithms operate on basically binary images where there are only two levels of interest: the black pixels represent ridges, and the white pixels represent valleys. Binarisation [1] converts a greylevel image into a binary image. This helps in improving the contrast between the ridges and valleys in a ? ngerprint image, and consequently facilitates the extraction of minutiae.One very useful property of the Gabor ? lter is that it contains a DC component of zero, which indicates that the resulting ? ltered image has a zero mean pixel value. Hence, binarisation of the image can be done by using a global threshold of zero. Binarisation involves examining the grey-level value of every pixel in the enhanced image, and, if the grey-level value is greater than the prede? ned global threshold, then the pixel value is set to value one; else, it is set to zero. The outcome of binarisation is a binary image which contains two levels of i nformation, the background valleys and the foreground ridges. . 7 Thinning Thinning is a morphological operation which is used to remove selected foreground pixels from the binary images. A standard thinning algorithm from [1] is used, which performs this operation using two subiterations. The algorithm can be accessed by a software MATLAB via the ‘thin’ operation of the bwmorph function. Each subiteration starts by examining the neighborhood of every pixel in the binary image, and on the basis of a particular set of pixel-deletion criteria, it decides whether the pixel can be removed or not. These subiterations goes on until no more pixels can be removed.Figure 4. 2: (a)Original Image, (b)Enhanced Image, (c)Binarised Image, (d)Thinned Image Chapter 5 Feature Extraction After improving quality of the ? ngerprint image we extract features from binarised and thinned images. We extract reference point, minutiae and key(used for one to many matching). 5. 1 Finding the Refer ence Point Reference point is very important feature in advanced matching algorithms because it provides the location of origin for marking minutiae. We ? nd the reference point using the algorithm as in [2]. Then we ? nd the relative position of minutiae and estimate the orientation ? ld of the reference point or the singular point. The technique is to extract core and delta points using Poincare Index. The value of Poincare index is 180o , ? 180o and 0o for a core, a delta and an ordinary point respectively. Complex ? lters are used to produce blur at di? erent resolutions. Singular point (SP) or reference point is the point of maximum ? lter response of these ? lters applied on image. Complex ? lters , exp(im? ) , of order m (= 1 and -1) are used to produce ? lter response. Four level resolutions are used here:level 0, level 1, level 2, level 3.Level 3 is lowest resolution and level 0 is highest resolution. Only ? lters of ? rst order are used : h = (x + iy)m g(x, y) where g(x,y) is a gaussian de? ned as g(x, y) = exp? ((x2 + y 2 )/2? 2 ) and m = 1, ? 1. Filters are applied to the complex valued orientation tensor ? eld image z(x, y) = (fx + ify )2 and not directly to the image. Here f x is the derivative of the original image in the x-direction and f y is the derivative in the y-direction. To ? nd the position of a possible 35 36 CHAPTER 5. FEATURE EXTRACTION Figure 5. 1: Row 1: ? lter response c1k , k = 3, 2, and 1. Row 2: ? ter response c2k , k = 3, 2, and 1. SP in a ? ngerprint the maximum ? lter response is extracted in image c13 and in c23 (i. e. ?lter response at m = 1 and level 3 (c13 ) and at m = ? 1 and level 3 (c23 )). The search is done in a window computed in the previous higher level (low resolution). The ? lter response at lower level (high resolution) is used for ? nding response at higher level (low resolution). At a certain resolution (level k), if cnk (xj , yj ) is higher than a threshold an SP is found and its position (xj , yj ) and the complex ? lter response cnk (xj , yj ) are noted. 5. 2 5. 2. 1Minutiae Extraction and Post-Processing Minutiae Extraction The most commonly employed method of minutiae extraction is the Crossing Number (CN) concept [1] . This method involves the use of the skeleton image where the ridge ? ow pattern is eight-connected. The minutiae are extracted by scanning the local neighborhood of each ridge pixel in the image using a 3 x 3 window. The CN value is then computed, which is de? ned as half the sum of the di? erences between pairs of adjacent pixels in the eight-neighborhood. Using the properties of the CN as shown in ? gure 5, the ridge pixel can then be classi? d as a ridge ending, bifurcation or non-minutiae point. For example, a ridge pixel with a CN of one corresponds to a ridge ending, and a CN of three corresponds to a bifurcation. 5. 2. MINUTIAE EXTRACTION AND POST-PROCESSING Table 5. 1: Properties of Crossing Number CN Property 0 Isolated Point 1 Ridge Ending Point 2 Continu ing Ridge Point 3 Bifurcation Point 4 Crossing Point 37 Figure 5. 2: Examples of (a)ridge-ending (CN=1) and (b)bifurcation pixel (CN=3) 5. 2. 2 Post-Processing False minutiae may be introduced into the image due to factors such as noisy images, and image artefacts created by the thinning process.Hence, after the minutiae are extracted, it is necessary to employ a post-processing [1] stage in order to validate the minutiae. Figure 5. 3 illustrates some examples of false minutiae structures, which include the spur, hole, triangle and spike structures . It can be seen that the spur structure generates false ridge endings, where as both the hole and triangle structures generate false bifurcations. The spike structure creates a false bifurcation and a false ridge ending point. Figure 5. 3: Examples of typical false minutiae structures : (c)Triangle, (d)Spike (a)Spur, (b)Hole, 38 CHAPTER 5.FEATURE EXTRACTION 5. 2. 3 Removing Boundary Minutiae For removing boundary minutiae, we used pixel- density approach. Any point on the boundary will have less white pixel density in a window centered at it, as compared to inner minutiae. We calculated the limit, which indicated that pixel density less than that means it is a boundary minutiae. We calculated it according to following formula: limit = ( w w ? (ridgedensity)) ? Wf req 2 (5. 1) where w is the window size, Wf req is the window size used to compute ridge density. Figure 5. 4: Skeleton of window centered at boundary minutiaeFigure 5. 5: Matrix Representation of boundary minutiae Now, in thinned image, we sum all the pixels in the window of size w centered at the boundary minutiae. If sum is less than limit, the minutiae is considered as boundary minutiae and is discarded. 5. 3. EXTRACTION OF THE KEY 39 5. 3 5. 3. 1 Extraction of the key What is key? Key is used as a hashing tool in this project. Key is small set of few minutiae closest to reference point. We match minutiae sets, if the keys of sample and query ? ngerprin ts matches. Keys are stored along with minutiae sets in the database.Advantage of using key is that, we do not perform full matching every time for non-matching minutiae sets, as it would be time consuming. For large databases, if we go on matching full minutiae set for every enrolled ? ngerprint, it would waste time unnecessarily. Two types of keys are proposed – simple and complex. Simple key has been used in this project. Figure 5. 6: Key Representation Simple Key This type of key has been used in this project. Minutiae which constitute this key are ten minutiae closest to the reference point or centroid of all minutiae, in sorted 40 CHAPTER 5. FEATURE EXTRACTION order. Five ? lds are stored for each key value i. e. (x, y, ? , t, r). (x, y) is the location of minutiae, ? is the value of orientation of ridge related to minutia with respect to orientation of reference point, t is type of minutiae, and r is distance of minutiae from origin. Due to inaccuracy and imperfection of reference point detection algorithm, we used centroid of all minutiae for construction of key. Complex Key The complex key stores more information and is structurally more complex. It stores vector of minutiae in which next minutiae is closest to previous minutiae, starting with reference point or centroid of all minutiae.It stores < x, y, ? , t, r, d, ? >. Here x,y,t,r,? are same, d is distance from previous minutiae entry and ? is di? erence in ridge orientation from previous minutiae. Data: minutiaelist = Minutiae Set, refx = x-cordinate of centroid, refy = y-cordinate of centroid Result: Key d(10)=null; for j = 1 to 10 do for i = 1 to rows(minutiaelist) do d(i) Chapter 6 Partitioning of Database Before we partition the database, we perform gender estimation and classi? cation. 6. 1 Gender Estimation In [3], study on 100 males and 100 females revealed that signi? cant sex di? erences occur in the ? ngerprint ridge density.Henceforth, gender of the candidate can be estimated on the basis of given ? ngerprint data. Henceforth, gender of the candidate can be estimated on the basis of given ? ngerprint data. Based on this estimation, searching for a record in the database can be made faster. Method for ? nding mean ridge density and estimated gender: The highest and lowest values for male and female ridge densities will be searched. If ridge density of query ? ngerprint is less than the lowest ridge density value of females, the query ? ngerprint is obviously of a male. Similarly, if it is higher than highest ridge density value of males, the query ? gerprint is of a female. So the searching will be carried out in male or female domains. If the value is between these values, we search on the basis of whether the mean of these values is less than the density of query image or higher. 41 42 CHAPTER 6. PARTITIONING OF DATABASE Figure 6. 1: Gender Estimation 6. 1. GENDER ESTIMATION Data: Size of Database = N; Ridge Density of query ? ngerprint = s Result: Estima ted Gender i. e. male or female maleupperlimit=0; femalelowerlimit=20; mean=0; for image < femalelowerlimit then femalelowerlimit 43 if s < maleupperlimit then estimatedgender 44 CHAPTER 6.PARTITIONING OF DATABASE 6. 2 Classi? cation of Fingerprint We divide ? ngerprint into ? ve classes – arch or tented arch, left loop, right loop, whorl and unclassi? ed. The algorithm for classi? cation [4] is used in this project. They used a ridge classi? cation algorithm that involves three categories of ridge structures:nonrecurring ridges, type I recurring ridges and type II recurring ridges. N1 and N2 represent number of type I recurring ridges and type II recurring ridges respectively. Nc and Nd are number of core and delta in the ? ngerprint. To ? nd core and delta, separate 135o blocks from orientation image. 35o blocks are shown in following ? gures. Figure 6. 2: 135o blocks of a ? ngerprint Based on number of such blocks and their relative positions, the core and delta are found using Poincare index method. After these, classi? cation is done as following: 1. If (N2 > 0) and (Nc = 2) and (Nd = 2), then a whorl is identi? ed. 2. If (N1 = 0) and (N2 = 0) and (Nc = 0) and (Nd = 0), then an arch is identi? ed. 3. If (N1 > 0) and (N2 = 0) and (Nc = 1) and (Nd = 1), then classify the input using the core and delta assessment algorithm[4]. 4. If (N2 > T2) and (Nc > 0), then a whorl is identi? ed. 5.If (N1 > T1) and (N2 = 0) and (Nc = 1) then classify the input using the core and delta assessment algorithm[4]. 6. If (Nc = 2), then a whorl is identi? ed. 7. If (Nc = 1) and (Nd = 1), then classify the input using the core and delta assessment algorithm[4]. 8. If (N1 > 0) and (Nc = 1), then classify the input using the core and delta assessment algorithm. 6. 3. PARTITIONING 9. If (Nc = 0) and (Nd = 0), then an arch is identi? ed. 10. If none of the above conditions is satis? ed, then reject the ? ngerprint. 45 Figure 6. 3: Fingerprint Classes (a)Left Loop, (b)Right Lo op, (c)Whorl, (d1)Arch, (d2)Tented Arch . 3 Partitioning After we estimate gender and ? nd the class of ? ngerprint, we know which ? ngerprints to be searched in the database. We roughly divide database into one-tenth using the above parameters. This would roughly reduce identi? cation time to one-tenth. 46 CHAPTER 6. PARTITIONING OF DATABASE Figure 6. 4: Partitioning Database Chapter 7 Matching Matching means ? nding most appropriate similar ? ngerprint to query ? ngerprint. Fingerprints are matched by matching set of minutiae extracted. Minutiae sets never match completely, so we compute match score of matching. If match score satis? s accuracy needs, we call it successful matching. We used a new key based one to many matching intended for large databases. 7. 1 Alignment Before we go for matching, minutiae set need to be aligned(registered) with each other. For alignment problems, we used hough transform based registration technique similar to one used by Ratha et al[5]. Minutiae alignment is done in two steps minutiae registration and pairing. Minutiae registration involves aligning minutiae using parameters < ? x, ? y, ? > which range within speci? ed limits. (? x, ? y) are translational parameters and ? is rotational parameter.Using these parameters, minutiae sets are rotated and translated within parameters limits. Then we ? nd pairing scores of each transformation and transformation giving maximum score is registered as alignment transformation. Using this transformation < ? x, ? y, ? >, we align query minutiae set with the database minutiae set. Algorithm is same as in [5] but we have excluded factor ? s i. e. the scaling parameter because it does not a? ect much the alignment process. ? lies from -20 degrees to 20 degrees in steps of 1 or 2 generalized as < ? 1 , ? 2 , ? 3 †¦? k > where k is number of rotations applied.For every query minutiae i we check if ? k + ? i = ? j where ? i and ? j are orientation 47 48 CHAPTER 7. MATCHING parameters of ith minutia of query minutiae set and j th minutia of database minutiae set. If condition is satis? ed, A(i,j,k) is ? agged as 1 else 0. For all these ? agged values, (? x, ? y) is calculated using following formula: ? (? x , ? y ) = qj ? ? cos? sin? ? ? ? pi , (7. 1) ?sin? cos? where qj and pi are the coordinates of j th minutiae of database minutiae set and ith minutiae of query minutiae set respectively. Using these < ? x, ?y, ? k > values, whole query minutiae set is aligned.This aligned minutiae set is used to compute pairing score. Two minutiae are said to be paired only when they lie in same bounding box and have same orientation. Pairing score is (number of paired minutiae)/(total number of minutiae). The i,j,k values which have highest pairing score are ? nally used to align minutiae set. Co-ordinates of aligned minutiae are found using the formula: ? qj = ? cos? sin? ? ? ? pi + (? x , ? y ), (7. 2) ?sin? cos? After alignment, minutiae are stored in sorted order of their di stance from their centroid or core. 7. 2 Existing Matching TechniquesMost popular matching technique of today is the simple minded n2 matching where n is number of minutiae. In this matching each minutiae of query ? ngerprint is matched with n minutiae of sample ? ngerprint giving total number of n2 comparisons. This matching is very orthodox and gives headache when identi? cation is done on large databases. 7. 3 One to Many matching Few algorithms are proposed by many researchers around the world which are better than normal n2 matching. But all of them are one to one veri? cation or one to one identi? cation matching types. We developed a one to many matching technique which uses key as the hashing tool.Initially, we do not match minutiae sets instead we per- 7. 3. ONE TO MANY MATCHING 49 form key matching with many keys of database. Those database ? ngerprints whose keys match with key of query ? ngerprint, are allowed for full minutiae matching. Key matching and full matching ar e performed using k*n matching algorithm discussed in later section. Following section gives method for one to many matching. Data: Query Fingerprint; Result: Matching Results; Acquire Fingerprint, Perform Enhancement, Find Fingerprint Class, Extract Minutiae, Remove Spurious and Boundary Minutiae, Extract Key,Estimate Gender; M . 3. 1 Method of One to Many Matching The matching algorithm will be involving matching the key of the query ? ngerprint with the many(M) keys of the database. Those which matches ,their full matching will be processed, else the query key will be matched with next M keys and so on. 50 Data: Gender, Class, i; Result: Matching Results; egender CHAPTER 7. MATCHING if keymatchstatus = success then eminutiae 7. 4 Performing key match and full matching Both key matching and full matching are performed using our k*n matching technique. Here k is a constant(recommended value is 15) chosen by us.In this method, we match ith minutiae of query set with k unmatched minu tiae of sample set. Both the query sets and sample sets must be in sorted order of distance from reference point or centroid. ith minutia of query minutiae list is matched with top k unmatched minutiae of database minutiae set. This type of matching reduces matching time of n2 to k*n. If minutiae are 80 in number and we chose k to be 15, the total number of comparisons will reduce from 80*80=6400 to 80*15=1200. And this means our matching will be k/n times faster than n2 matching. 7. 5. TIME COMPLEXITY OF THIS MATCHING TECHNIQUE 51 Figure 7. : One to Many Matching 7. 5 Time Complexity of this matching technique Let s = size of the key, n = number of minutiae, N = number of ? ngerprints matched till successful identi? cation, k = constant (see previous section). There would be N-1 unsuccessful key matches, one successful key match, one successful full match. Time for N-1 unsuccessful key matches is (N-1)*s*k (in worst case), for successful full match is s*k and for full match is n*k. Total time is (N-1)*s*k+n*k+s*k = k(s*N+n). Here s=10 and we have reduced database to be searched to 1/10th ,so N matching technique, it would have been O(Nn2 ).For large databases, our matching technique is best to use. Averaging for every ? ngerprint, we have O(1+n/N) in this identi? cation process which comes to O(1) when N >> n. So we can say that our identi? cation system has constant average matching time when database size is millions. Chapter 8 Experimental Analysis 8. 1 Implementation Environment We tested our algorithm on several databases like FVC2004, FVC2000 and Veri? nger databases. We used a computer with 2GB RAM and 1. 83 GHz Intel Core2Duo processor and softwares like Matlab10 and MSAccess10. 8. 2 8. 2. 1 Fingerprint Enhancement Segmentation and NormalizationSegmentation was performed and it generated a mask matrix which has values as 1 for ridges and 0 for background . Normalization was done with mean = 0 and variance = 1 (? g 8. 1). Figure 8. 1: Normalized Image 52 8. 2. FINGERPRINT ENHANCEMENT 53 8. 2. 2 Orientation Estimation In orientation estimation, we used block size = 3*3. Orientations are shown in ? gure 8. 2. Figure 8. 2: Orientation Image 8. 2. 3 Ridge Frequency Estimation Ridge density and mean ridge density were calculated. Darker blocks indicated low ridge density and vice-versa. Ridge frequencies are shown in ? gure 8. 3. Figure 8. 3: Ridge Frequency Image 8. 2. 4Gabor Filters Gabor ? lters were employed to enhance quality of image. Orientation estimation and ridge frequency images are requirements for implementing gabor ? lters. ?x and ? y are taken 0. 5 in Raymond Thai, but we used ? x = 0. 7 and ? y = 0. 7. Based on these values , we got results which were satis? able and are shown in ? gure 8. 4. 54 CHAPTER 8. EXPERIMENTAL ANALYSIS Figure 8. 4: Left-Original Image, Right-Enhanced Image 8. 2. 5 Binarisation and Thinning After the ? ngerprint image is enhanced, it is then converted to binary form, and submitted to the thinni ng algorithm which reduces the ridge thickness to one pixel wide.Results of binarisation are shown in ? gure 8. 5 and of thinning are shown in ? gure 8. 6. Figure 8. 5: Binarised Image 8. 3. FEATURE EXTRACTION 55 Figure 8. 6: Thinned Image 8. 3 8. 3. 1 Feature Extraction Minutiae Extraction and Post Processing Minutiae Extraction Using the crossing number method, we extracted minutiae. For this we used skeleton image or the thinned image. Due to low quality of ? ngerprint, a lot of false and boundary minutiae were found. So we moved forward for post-processing step. Results are shown in ? gure 8. 7 and 8. 8. Figure 8. 7: All Extracted Minutiae 56 CHAPTER 8. EXPERIMENTAL ANALYSISFigure 8. 8: Composite Image with spurious and boundary minutiae After Removing Spurious and Boundary Minutiae False minutiae were removed using method described in earlier section. For removing boundary minutiae, we employed our algorithm which worked ? ne and minutiae extraction results are shown in table 8 . 2. Results are shown in ? gure 8. 9 and 8. 10. Figure 8. 9: Minutiae Image after post-processing As we can see from table 8. 2 that removing boundary minutiae considerably reduced the number of false minutiae from minutiae extraction results. 8. 4. GENDER ESTIMATION AND CLASSIFICATION 57 Figure 8. 0: Composite Image after post-processing Table 8. 1: Average Number of Minutiae before and after post-processing DB After After Removing After Removing Used Extraction Spurious Ones Boundary Minutiae FVC2004DB4 218 186 93 FVC2004DB3 222 196 55 8. 3. 2 Reference Point Detection For reference point extraction we used complex ? lters as described earlier. For a database size of 300, reference point was found with success rate of 67. 66 percent. 8. 4 8. 4. 1 Gender Estimation and Classi? cation Gender Estimation Average ridge density was calculated along with minimum and maximum ridge densities shown in table 8. . Mean ridge density was used to divide the database into two parts. This reduce d database size to be searched by half. Based on the information available about the gender of enrolled student, we can apply our gender estimation algorithm which will further increase the speed of identi? cation. 8. 4. 2 Classi? cation Fingerprint classi? cation was performed on both original and enhanced images. Results were more accurate on the enhanced image. We used same algorithm as in sec 6. 2 to classify the ? ngerprint into ? ve classes – arch, left loop, right loop, whorl and 58 CHAPTER 8.EXPERIMENTAL ANALYSIS Figure 8. 11: Plotted Minutiae with Reference Point(Black Spot) Table 8. 2: Ridge Density Calculation Results Window Minimum Maximum Mean Total Average Size Ridge Ridge Ridge Time Time Taken Density Density Density Taken Taken 36 6. 25 9. 50 7. 87 193. 76 sec 1. 46 sec unclassi? ed. This classi? cation was used to divide the database into ? ve parts which would reduce the database to be searched to one-? fth and ultimately making this identi? cation process ? ve times faster. Results of classi? cation are shown in table 8. 4, 8. 5 and 8. 6. 8. 5 EnrollingAt the time of enrolling personal details like name, semester, gender, age, roll number etc. were asked to input by the user and following features of ? ngerprint were saved in the database (1)Minutiae Set (2)Key (3)Ridge Density (4)Class Total and average time taken for enrolling ? ngerprints in database is shown in table 8. 6. MATCHING Table 8. 3: Classi? cation Results on Original Image Class No. of (1-5) Images 1 2 2 2 3 3 4 4 5 121 Table 8. 4: Classi? cation Results on Enhanced Image Class No. of (1-5) Images 1 8 2 3 3 3 4 6 5 112 59 8. 7. All the personal details were stored in the MS Access database and were modi? d by running sql queries inside matlab. Fingerprint features were stored in txt format inside a separate folder. When txt ? le were used, the process of enrolling was faster as compared to storing the values in MS Access DB. It was due to the overhead of connections, ru nning sql queries for MS Access DB. 8. 6 Matching Fingerprint matching is required by both veri? cation and identi? cation processes. 8. 6. 1 Fingerprint Veri? cation Results Fingerprint veri? cation is the process of matching two ? ngerprints against each other to verify whether they belong to same person or not. When a ? gerprint matches with the ? ngerprint of same individual, we call it true accept or if it doesn’t, we call it false reject. In the same way if the ? ngerprint of di? erent individuals match, we call it a false accept or if it rejects them, it is true reject. False Accept Rate (FAR) and False Reject Rate (FRR) are the error rates which are used to express matching trustability. FAR is de? ned by the formula : 60 CHAPTER 8. EXPERIMENTAL ANALYSIS Table 8. 5: Time taken for Classi? cation Image Average Total Taken Time(sec) Time(sec) Original 0. 5233 69. 07 Enhanced 0. 8891 117. 36 Table 8. : Time taken for Enrolling No. of Storage Average Total Images Type Tim e(sec) Time(hrs) 294 MS Access DB 24. 55 2. 046 60 MS Access DB 29. 37 0. 49 150 TXT ? les 15. 06 1. 255 F AR = FA ? 100, N (8. 1) FA = Number of False Accepts, N = Total number of veri? cations FRR is de? ned by the formula : FR ? 100, N F RR = (8. 2) FR = Number of False Rejects. FAR and FRR calculated over six templates of Veri? nger DB are shown in table 8. 8. This process took approximately 7 hours. 8. 6. 2 Identi? cation Results and Comparison with Other Matching techniques Fingerprint identi? cation is the process of identifying a query ? gerprint from a set of enrolled ? ngerprints. Identi? cation is usually a slower process because we have to search over a large database. Currently we match minutiae set of query ? ngerprint with the minutiae sets of enrolled ? ngerprints. In this project, we store key in the database at the time of enrolling. This key as explained in sec 5. 3 helps in 8. 6. MATCHING Table 8. 7: Error Rates FAR FRR 4. 56 12. 5 14. 72 4. 02 61 Figure 8. 12: G raph: Time taken for Identi? cation vs Size of Database(key based one to many identi? cation) reducing matching time over non-matching ? ngerprints. For non-matching enrolled ? gerprints, we don’t perform full matching, instead a key matching. Among one or many keys which matched in one iteration of one to many matching, we allow full minutiae set matching. Then if any full matching succeeds, we perform post matching steps. This identi? cation scheme has lesser time complexity as compared to conventional n2 one to one identi? cation. Identi? cation results are shown in table 8. 9. The graph of time versus N is shown in ? gure 8. 13. Here N is the index of ? ngerprint to be identi? ed from a set of enrolled ? ngerprints. Size of database of enrolled ? ngerprints was 150. So N can vary from