A measure of information gained through biometric systems

We propose a measure of information gained through biometric matching systems. Firstly, we discuss how the information about the identity of a person is derived from biometric samples through a biometric system, and define the "biometric system entropy" or BSE based on mutual information. We present several theoretical properties and interpretations of the BSE, and show how to design a biometric system which maximizes the BSE. Then we prove that the BSE can be approximated asymptotically by the relative entropy D(fG(x)Â?fI(x)) where fG(x) and fI(x) are probability mass functions of matching scores between samples from individuals and among population. We also discuss how to evaluate the BSE of a biometric system and show experimental evaluation of the BSE of face, fingerprint and multimodal biometric systems. We propose an information theoretical measure for biometric systems.We present several useful properties and interpretations of the measure.We prove that the measure can be approximated by the relative entropy.We discuss how to evaluate the measure and show experimental evaluations.

[1]  Igor Vajda,et al.  Estimation of the Information by an Adaptive Partitioning of the Observation Space , 1999, IEEE Trans. Inf. Theory.

[2]  Kenta Takahashi,et al.  A Metric of Information Gained Through Biometric Systems , 2010, ICPR 2010.

[3]  Richard Youmaran,et al.  Towards a Measure of Biometric Information , 2006, 2006 Canadian Conference on Electrical and Computer Engineering.

[4]  Ajay Kumar,et al.  On estimating performance indices for biometric identification , 2009, Pattern Recognit..

[5]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[6]  John Daugman,et al.  The importance of being random: statistical principles of iris recognition , 2003, Pattern Recognit..

[7]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[8]  Qing Wang,et al.  Divergence estimation of continuous distributions based on data-dependent partitions , 2005, IEEE Transactions on Information Theory.

[9]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[10]  Nasir D. Memon,et al.  How to Measure Biometric Information? , 2010, 2010 20th International Conference on Pattern Recognition.

[11]  M. N. Goria,et al.  A new class of random vector entropy estimators and its applications in testing statistical hypotheses , 2005 .

[12]  Sergio Verdú,et al.  Generalizing the Fano inequality , 1994, IEEE Trans. Inf. Theory.

[13]  Lujo Bauer,et al.  Encountering stronger password requirements: user attitudes and behaviors , 2010, SOUPS.

[14]  R. Bass,et al.  Review: P. Billingsley, Convergence of probability measures , 1971 .

[15]  R. Bartle.  The elements of integration and Lebesgue measure , 1995 .

[16]  Qing Wang,et al.  Divergence Estimation for Multidimensional Densities Via $k$-Nearest-Neighbor Distances , 2009, IEEE Transactions on Information Theory.

[17]  L. Pronzato,et al.  ESTIMATION OF ENTROPIES AND DIVERGENCES VIA NEAREST NEIGHBORS , 2006 .

[18]  L. Györfi,et al.  Nonparametric entropy estimation. An overview , 1997 .

[19]  Jacob Goldberger,et al.  ICA based on a Smooth Estimation of the Differential Entropy , 2008, NIPS.

[20]  Sanjeev R. Kulkarni,et al.  A Nearest-Neighbor Approach to Estimating Divergence between Continuous Random Vectors , 2006, 2006 IEEE International Symposium on Information Theory.

[21]  Kenta Takahashi,et al.  Accuracy improvement with high convenience in biometric identification using multihypothesis sequential probability ratio test , 2009, 2009 First IEEE International Workshop on Information Forensics and Security (WIFS).

[22]  J. L. Wayman,et al.  Best practices in testing and reporting performance of biometric devices. , 2002 .

[23]  Fernando Pérez-Cruz,et al.  Kullback-Leibler divergence estimation of continuous distributions , 2008, 2008 IEEE International Symposium on Information Theory.

[24]  E. Lehmann.  Testing Statistical Hypotheses , 1960 .

[25]  Ray A. Perlner,et al.  Electronic Authentication Guideline , 2014 .

[26]  Arun Ross,et al.  Fusion in Multibiometric Identification Systems: What about the Missing Data? , 2009, ICB.

[27]  Naohisa Komatsu,et al.  A metric of identification performance of biometrics based on information content , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.