论文信息 - A Metric of Information Gained Through Biometric Systems

A Metric of Information Gained Through Biometric Systems

We propose a metric 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. Then we prove that the BSE can be approximated asymptotically by the Kullback-Leibler divergence $D(f_G(x) | | f_I(x))$ where $f_G(x), f_I(x)$ are PDFs of matching scores between samples from an individuals and among population. We also discuss how to evaluate $D(f_G | | f_I)$ of a biometric system and show a numerical example of face and fingerprint matching systems.

Kenta Takahashi | Takao Murakami

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

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

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

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

[5] 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).

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