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Abstract
Many of the performance evaluation techniques for biometric authentication use error probabilities to yield a measure called receiver operating characteristic (ROC). The ROC is based on the Neyman-Pearson hypothesis testing and is obtained by varying a threshold for decision making. This measure is dependent on database partitioning and choice of thresholds. Also, obtaining the probability distributions, and thus the ROC, is computationally complex. Recent approaches based on information theoretic models partially overcome these limitations and also provide insight into the performance of biometric authentication techniques. Measures in line with obtaining Chernoff capacity and with Shannon capacity have been proposed, and are respectively called recognition capacity and constrained capacity. Measures which are largely independent of data size and quality are based on the minimization of false matches between templates and are good indicators of biometrics uniqueness or, equivalently, its random correspondence. The parameters related to confidence intervals are however obtained empirically. One such measure is obtained from the probability distribution of the Hamming distance between templates of the iris biometrics. Also, relative entropy of features between a user and the population yields a measure of uniqueness for face biometrics. Another approach is to measure the probability of this random correspondence (PRC). PRC of a fingerprint biometrics has been obtained using compound statistical distributions. Another formulation of PRC is based on a rate-distortion framework by applying a distortion constraint to a codebook of binarized features. Biometric templates are akin to noisy source symbols in an information theoretic setup. A bound on the PRC has been obtained by developing error exponents of noisy biometrics represented in terms of a binary source-channel model. The method has low computational complexity, is not limited to a specific biometrics, and does not require empirically obtained confidence intervals.
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Notes on contributors
Jay R. Bhatnagar
Jay R. Bhatnagar received B.Engg (Hons.) in Electronics engineering from Pune University, his M.S.E.E. from the University of Southern California, CSI and has submitted his Doctoral thesis in Electrical engineering, IIT Delhi. In his PhD work, he has investigated applications of Information theory to performance related issues in Pattern Recognition systems. Earlier, he has worked as an Assistant engineer (Network -2004 he has worked as University of California Dean’s Fellow in Electrical engineering, U.C. Riverside and as Research Staff in GCATT, Georgia Tech. His research interests are theoretical in nature based in: Pattern recognition, Biometrics, Communication theory, Applied Probability & Statistics and Information Theory.
E-mail: [email protected]
Brejesh Lall
Brejesh Lall received the B.E. degree in electronics and communication engineering from the Delhi College of Engineering, Delhi, India in 1991, M.E. in electronics and communication engineering also from Delhi College of Engineering, Delhi, India in 1992 and the Ph.D. degree from the Indian Institute of Technology Delhi, India in 1999. He joined Hughes Software Systems, Gurgaon, India in 1997 and worked there in the Signal Processing group till 2005. In 2005 he joined Indian Institute of Technology Delhi where he is currently an Assistant Professor. His research interests include Multirate Signal Processing, Image Processing and Wireless communication.
E-mail: [email protected]
R. K. Patney
R. K. Patney received his PhD in Electrical engineering from IIT Delhi and is currently Professor of Electrical engineering, IIT Delhi. His research interest is Digital signal processing and applications.
E-mail: [email protected]
