Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Biometric Identification Techniques

Biometric Identification Techniques
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Author(s): Hunny Mehrotra (Indian Institute of Technology Kanpur, India), Pratyush Mishra (Indian Institute of Technology Kanpur, India) and Phalguni Gupta (Indian Institute of Technology Kanpur, India)
Copyright: 2009
Pages: 7
Source title: Encyclopedia of Information Science and Technology, Second Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-60566-026-4.ch060


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In today’s high-speed world, millions of transactions occur every minute. For these transactions, data need to be readily available for the genuine people who want to have access, and it must be kept securely from imposters. Some methods of establishing a person’s identity are broadly classified into: 1. Something You Know: These systems are known as knowledge-based systems. Here the person is granted access to the system using a piece of information like a password, PIN, or your mother’s maiden name. 2. Something You Have: These systems are known as token-based systems. Here a person needs a token like a card key, smartcard, or token (like a Secure ID card). 3. Something You Are: These systems are known as inherited systems like biometrics. This refers to the use of behavioral and physiological characteristics to measure the identity of an individual. The third method of authentication is preferred over token-based and knowledge-based methods, as it cannot be misplaced, forgotten, stolen, or hacked, unlike other approaches. Biometrics is considered as one of the most reliable techniques for data security and access control. Among the traits used are fingerprints, hand geometry, handwriting, and face, iris, retinal, vein, and voice recognition. Biometrics features are the information extracted from biometric samples which can be used for comparison. In cases of face recognition, the feature set comprises detected landmark points like eye-to-nose distance, and distance between two eye points. Various feature extraction methods have been proposed, for example, methods using neural networks, Gabor filtering, and genetic algorithms. Among these different methods, a class of methods based on statistical approaches has recently received wide attention. In cases of fingerprint identification, the feature set comprises location and orientation of ridge endings and bifurcations, known as a minutiae matching approach (Hong, Wan, & Jain, 1998). Most iris recognition systems extract iris features using a bank of filters of many scales and orientation in the whole iris region. Palmprint recognition, just like fingerprint identification, is based on aggregate information presented in finger ridge impression. Like fingerprint identification, three main categories of palm matching techniques are minutiae-based matching, correlation-based matching, and ridge-based matching. The feature set for various traits may differ depending upon the extraction mechanism used. The system that uses a single trait for authenticity veri- fication is called unimodal biometric system. A unimodal biometric system (Ross & Jain, 2003) consists of three major modules: sensor module, feature extraction module, and matching module. However, even the best biometric traits face numerous problems like non-universality, susceptibility to biometric spoofing, and noisy input. Multimodal biometrics provides a solution to the above mentioned problems. A multimodal biometric system uses multiple sensors for data acquisition. This allows capturing multiple samples of a single biometric trait (called multi-sample biometrics) and/or samples of multiple biometric traits (called multi-source or multimodal biometrics). This approach also enables a user who does not possess a particular biometric identifier to still enroll and authenticate using other traits, thus eliminating the enrollment problems. Such systems, known as multimodal biometric systems (Tolba & Rezq, 2000), are expected to be more reliable due to the presence of multiple pieces of evidence. A good fusion technique is required to fuse information for such biometric systems.

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