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Biometric recognition refers to an automatic recognition of individuals based on feature vector(s) derived from their physiological and/or behavioral characteristic. Biometric recognition systems should provide a reliable personal recognition schemes to either confirmer determine the identity of an individual. Applications of such a system include computer systems security, secure electronic banking, mobile phones, credit cards, secure access to buildings, health and social services. By using biometrics a person could be identified basedon "who she/he is" rather than "what she/he has" (card, token, key) or "what she/he knows" (Password, PIN). In this paper, a brief overview of biometric methods, both Unimodal andMultimodal, and their advantages and disadvantages are presented. The term "biometrics" is derived from the Greek words bio (life) and metric (to measure). Biometrics refers to the automatic identification of a person based on his/her physiological or behavioral characteristics. This method of identification is preferred over traditional methods involving passwords and PIN numbers for its accuracy and case sensitiveness. A biometric system is essentially a pattern recognition system which makes a personal identification by determining the authenticity of a specific physiological or behavioral characteristic possessed by the user. An important issue in designing a practical system is to determine how an individual is identified. Depending on the context, a biometric system can be either a verification (authentication) system or an identification system. Verification involves confirming or denying a person's claimed identity while in identification, one has to establish a person's identity. Biometric systems are divided on the basis of the authentication medium used. They are broadly divided as identifications of Hand Geometry, Vein Pattern, Voice Pattern, DNA, Signature Dynamics, Finger Prints, Iris Pattern and Face Detection. These methods are used on the basis of the scope of the testing medium, the accuracy required and speed required. Every medium of authentication has its own advantages and shortcomings. With the increased use of computers as vehicles of information technology, it is necessary to restrict unauthorized access to or fraudulent use of sensitive/personal data. Biometric techniques being potentially able to augment this restriction are enjoying a renewed interest.
Biometric security system identify a human from a measurement of a physical characteristics or behavioral trait (for example, iris scan, hand geometry, retinal scan, DNA sequence characteristics, finger prints, hand written signature, facial characteristics and voice prints) to determine an identity. There is an typical enrollment process in which the biological information is stored in the database for future identification or verification process.
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Book Review of "Biometrics. Bodies, Technologies, Biopolitics. Written by Joseph Pugliese. Published by Routledge, 2010.
A shape invariant model for functions f 1 ,…,f n specifies that each individual function f i can be related to a common shape function g through the relation f i (x)=a i g(c i x + d i ) + b i . We consider a flexible mixture model that allows multiple shape functions g 1 ,…,g K , where each f i is a shape invariant transformation of one of those g k . We derive an MCMC algorithm for fitting the model using Bayesian Adaptive Regression Splines (BARS), propose a strategy to improve its mixing properties and utilize existing model selection criteria in semiparametric mixtures to select the number of distinct shape functions. We discuss some of the computational difficulties that arise. The method is illustrated using synaptic transmission data, where the groups of functions may indicate different active zones in a synapse.
Integrating Multiple Cues in Biometric Systems SACHIN KUMAR
Biometrics provides a better solution for increasing security requirements and privacy protection than traditional recognition methods such as passwords and PINs. Figure 2.1 shows a biometric system that uses a single biometric trait to establish identity is known as unibiometric system. Various limitations imposed by such biometric systems are noisy data, non-universality, intra-class variations, inter-class similarities and spoof attacks. These limitations can be alleviated by fusing the information presented by multiple sources. A system that consolidates the evidence presented by multiple biometric sources is known as a multibiometric system. Figure 2.2 shows a typical multibiometric system. Integration of evidences is called information fusion. If fusion is appropriately done then it can enhance the matching accuracy of a recognition system. These systems are also expected to be more reliable due to the availability of multiple pieces of evidence. Combining multiple sources of information into a system, improves matching performance, increases population coverage and deters spoofing activities.Mere using multiple biometrics does not imply better system performance rather degrades the performance of individual biometric traits when used in poorly designed system [HON, 1999].
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