What is Biometrics?

"Biometrics is the development of statistical and mathematical methods applicable to data analysis problems in the biological sciences

With regard to technology Biometrics is the term given to the use of biological traits or behavioural characteristics to identify an individual. Their traits may be fingerprints, hand geometry, facial geometry, retina patterns, iris patterns, voice recognition, handwriting recognition. A Biometrics system is basically a pattern recognition system, including all the hardware and associated software and the interconnecting infrastructure, enabling identification by matching a live sample to a stored pattern in a database. When resolving an individual’s identity there is a distinction between verification and identification and different Biometric systems fall into these two categories. Each sub-category resolves a different question. The first, verification, involves confirming or denying an individual’s claimed identity - ‘Am I who I claim I am?’ The second, identification, involves establishing an individual’s identity - ‘Who am I?’ By resolving these questions using biometrics these systems go beyond traditional security methods, by insisting that the person trying to log on is the actual person. Biometrics is irrevocably tied to the individual.

With regard to computer networks, Biometrics can be used to automatically authenticate an individual using their distinguishable traits. This security offers increased confidence levels for users of the network, providing the system is correctly implemented and utilized. The Network can be exploited fully without fear of a security breach. Biometric secure Systems on the web would make the popular targets of banking data, business intelligence, credit card numbers, medical information and other personal data transactions on the web more secure and thus increase the populations confidence in using these methods, increasing e-commerce confidence and thus enabling it to reach its full potential.

Biometrics is also being called upon in the Cellular phone industry, where the companies are vulnerable to cloning, where new phones are created using a stolen number, and new subscription fraud, where a phone is obtained using a false identity. Here Biometrics could be used on the handheld set to recognise ownership, and a biometric trait could be taken at authentication.

Biometrics can be used to secure transactions at automatic teller machines, no longer requiring the presentation of an ATM card (a biometric is hard to steal). It could also be used for transactions at point of sale. Other markets include telephone banking and Internet Banking. Biometrics can be used in any Network where the utmost security is needed. It doesn’t just provide security because the physiological traits between people are unique (PIN numbers should also be unique), but also because these traits cannot be interchanged between people.
The fundamental argument for using Biometrics for Network authentication is the increase in security while eliminating the extras such as PIN, passwords and smart cards, which can get into the wrong hands and do a lot of damage to a network, which is then not able to run at its full capacity until the security breach has been amended.

In the workplace passwords and logins are often passed between co-workers, written down for convenience or reused multiple times for different networks. Biometric logins would make it unfeasible for anyone, other than the intended login, to login to the network. So for every worker, if they were to use the network an account must be set up for them. The workers cannot forget their password, which may be one of several passwords, because their password into the system is a physiological trait.

There are of course security issues with Biometrics that must be addressed. Where will the data be stored? Are you authenticating an actual live sample or just authenticating a message? Can the same Biometric be used for multiple different systems? Will the system be securely implemented? These questions will be addressed in this paper. If Biometrics for network authentication is accepted into society, in the future we may be paying for our groceries at the supermarket on credit with a laser scan of the iris - physical method of access and payment may become a thing of the past.

How are biometrics used in networks

The most obvious use of biometrics for network security is for secure workstation logons for a workstation connected to a network. Each workstation requires some software support for biometric identification of the user as well as, depending on the biometric being used, some hardware device. The cost of hardware devices is one thing that may lead to the widespread use of voice biometric security identification, especially among companies and organizations on a low budget. Hardware device such as computer mice with built in thumbprint readers would be the next step up. These devices would be more expensive to implement on several computers, as each machine would require its own hardware device. A biometric mouse, with the software to support it, is available from around $120 in the U.S. The advantage of voice recognition software is that it can be centralized, thus reducing the cost of implementation per machine. At top of the range a centralized voice biometric package can cost up to $50,000 but may be able to manage the secure log-on of up to 5000 machines.

The main use of Biometric network security will be to replace the current password system. Maintaining password security can be a major task for even a small organization. Passwords have to be changed every few months and people forget their password or lock themselves out of the system by incorrectly entering their password repeatedly. Very often people write their password down and keep it near their computer (on a post-it note attached to the underside of the keyboard is a frequently seen favourite). This is of course completely undermines any effort at network security. Biometrics can replace these. For example the city of Glendale in Los Angeles county California replaced its password system with fingerprint scanners that use biometrics. The cities employees had the usual password problems. The passwords had to be changed every 90 days and no dictionary words were allowed, only 8-digit alphanumeric strings. The vast majority of users failed to change their passwords and as a result got locked out of the system. The only way for them to get back in the system was a call to the IT helpdesk, which became swamped with calls. The help desk staff ended up spending a disproportionably large amount of time fixing problems with passwords. This is the hidden cost of using passwords, the helpdesk admin costs that always result when people get locked out of the system. The use of biometric identification stops this problem and while it may be expensive to set up at first, these devices save on administration and user assistance costs.

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Database and SQL Related Ebooks

SQL for MySQL Developers: A Comprehensive Tutorial and ReferenceBy Rick F. van der
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Artificial Intelligence

.Here some eBooks this may be help full to you

http://www.flazx.info/scBvvBKz/0137903952.zip.htm




What is artificial intelligence?
A. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

Q. Yes, but what is intelligence?
A. Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.

Q. Isn't there a solid definition of intelligence that doesn't depend on relating it to human intelligence?
A. Not yet. The problem is that we cannot yet characterize in general what kinds of computational procedures we want to call intelligent. We understand some of the mechanisms of intelligence and not others.

Q. Is intelligence a single thing so that one can ask a yes or no question ``Is this machine intelligent or not?''?
A. No. Intelligence involves mechanisms, and AI research has discovered how to make computers carry out some of them and not others. If doing a task requires only mechanisms that are well understood today, computer programs can give very impressive performances on these tasks. Such programs should be considered ``somewhat intelligent''.

Q. Isn't AI about simulating human intelligence?
A. Sometimes but not always or even usually. On the one hand, we can learn something about how to make machines solve problems by observing other people or just by observing our own methods. On the other hand, most work in AI involves studying the problems the world presents to intelligence rather than studying people or animals. AI researchers are free to use methods that are not observed in people or that involve much more computing than people can do.

Q. What about IQ? Do computer programs have IQs?
A. No. IQ is based on the rates at which intelligence develops in children. It is the ratio of the age at which a child normally makes a certain score to the child's age. The scale is extended to adults in a suitable way. IQ correlates well with various measures of success or failure in life, but making computers that can score high on IQ tests would be weakly correlated with their usefulness. For example, the ability of a child to repeat back a long sequence of digits correlates well with other intellectual abilities, perhaps because it measures how much information the child can compute with at once. However, ``digit span'' is trivial for even extremely limited computers.
However, some of the problems on IQ tests are useful challenges for AI.

Q. What about other comparisons between human and computer intelligence?
Arthur R. Jensen [Jen98], a leading researcher in human intelligence, suggests ``as a heuristic hypothesis'' that all normal humans have the same intellectual mechanisms and that differences in intelligence are related to ``quantitative biochemical and physiological conditions''. I see them as speed, short term memory, and the ability to form accurate and retrievable long term memories.
Whether or not Jensen is right about human intelligence, the situation in AI today is the reverse.
Computer programs have plenty of speed and memory but their abilities correspond to the intellectual mechanisms that program designers understand well enough to put in programs. Some abilities that children normally don't develop till they are teenagers may be in, and some abilities possessed by two year olds are still out. The matter is further complicated by the fact that the cognitive sciences still have not succeeded in determining exactly what the human abilities are. Very likely the organization of the intellectual mechanisms for AI can usefully be different from that in people.
Whenever people do better than computers on some task or computers use a lot of computation to do as well as people, this demonstrates that the program designers lack understanding of the intellectual mechanisms required to do the task efficiently.

Q. When did AI research start?
A. After WWII, a number of people independently started to work on intelligent machines. The English mathematician Alan Turing may have been the first. He gave a lecture on it in 1947. He also may have been the first to decide that AI was best researched by programming computers rather than by building machines. By the late 1950s, there were many researchers on AI, and most of them were basing their work on programming computers.

Q. Does AI aim to put the human mind into the computer?
A. Some researchers say they have that objective, but maybe they are using the phrase metaphorically. The human mind has a lot of peculiarities, and I'm not sure anyone is serious about imitating all of them.

Q. What is the Turing test?
A. Alan Turing's 1950 article Computing Machinery and Intelligence [Tur50] discussed conditions for considering a machine to be intelligent. He argued that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. This test would satisfy most people but not all philosophers. The observer could interact with the machine and a human by teletype (to avoid requiring that the machine imitate the appearance or voice of the person), and the human would try to persuade the observer that it was human and the machine would try to fool the observer.

The Turing test is a one-sided test. A machine that passes the test should certainly be considered intelligent, but a machine could still be considered intelligent without knowing enough about humans to imitate a human.
Daniel Dennett's book Brainchildren [Den98] has an excellent discussion of the Turing test and the various partial Turing tests that have been implemented, i.e. with restrictions on the observer's knowledge of AI and the subject matter of questioning. It turns out that some people are easily led into believing that a rather dumb program is intelligent.

Q. Does AI aim at human-level intelligence?
A. Yes. The ultimate effort is to make computer programs that can solve problems and achieve goals in the world as well as humans. However, many people involved in particular research areas are much less ambitious.

Q. How far is AI from reaching human-level intelligence? When will it happen?
A. A few people think that human-level intelligence can be achieved by writing large numbers of programs of the kind people are now writing and assembling vast knowledge bases of facts in the languages now used for expressing knowledge.
However, most AI researchers believe that new fundamental ideas are required, and therefore it cannot be predicted when human level intelligence will be achieved.

Q. Are computers the right kind of machine to be made intelligent?
A. Computers can be programmed to simulate any kind of machine.
Many researchers invented non-computer machines, hoping that they would be intelligent in different ways than the computer programs could be. However, they usually simulate their invented machines on a computer and come to doubt that the new machine is worth building. Because many billions of dollars that have been spent in making computers faster and faster, another kind of machine would have to be very fast to perform better than a program on a computer simulating the machine.

Q. Are computers fast enough to be intelligent?
A. Some people think much faster computers are required as well as new ideas. My own opinion is that the computers of 30 years ago were fast enough if only we knew how to program them. Of course, quite apart from the ambitions of AI researchers, computers will keep getting faster.

Q. What about parallel machines?
A. Machines with many processors are much faster than single processors can be. Parallelism itself presents no advantages, and parallel machines are somewhat awkward to program. When extreme speed is required, it is necessary to face this awkwardness.

Q. What about making a ``child machine'' that could improve by reading and by learning from experience?
A. This idea has been proposed many times, starting in the 1940s. Eventually, it will be made to work. However, AI programs haven't yet reached the level of being able to learn much of what a child learns from physical experience. Nor do present programs understand language well enough to learn much by reading.

Q. Might an AI system be able to bootstrap itself to higher and higher level intelligence by thinking about AI?
A. I think yes, but we aren't yet at a level of AI at which this process can begin.

Q. What about chess?
A. Alexander Kronrod, a Russian AI researcher, said ``Chess is the Drosophila of AI.'' He was making an analogy with geneticists' use of that fruit fly to study inheritance. Playing chess requires certain intellectual mechanisms and not others. Chess programs now play at grandmaster level, but they do it with limited intellectual mechanisms compared to those used by a human chess player, substituting large amounts of computation for understanding. Once we understand these mechanisms better, we can build human-level chess programs that do far less computation than do present programs.
Unfortunately, the competitive and commercial aspects of making computers play chess have taken precedence over using chess as a scientific domain. It is as if the geneticists after 1910 had organized fruit fly races and concentrated their efforts on breeding fruit flies that could win these races.


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