TY - GEN
T1 - On some performance indices for biometric identification system
AU - Bhatnagar, Jay
AU - Pathak, Ajay Kumar
PY - 2007/12/1
Y1 - 2007/12/1
N2 - This paper investigates a new approach to formulate performance indices of biometric system using information theoretic models. The performance indices proposed here (unlike conventionally used FAR, GAR, DET etc.) are scalable in estimating performance of large scale biometric system. This work proposes a framework for identification capacity of a biometric system, along with insights on number of cohort users, capacity enhancements from user specific statistics etc. While incorporating feature level information in a rate-distortion framework, we derive condition for optimal feature representation. Furthermore, employing entropy measures to distance (hamming) distribution of the encoded templates, this paper proposes an upper bound for false random correspondence probability. Our analysis concludes that capacity can be the performance index of a biometric system while individuality expressed in false random correspondence can be the performance index of the biometric trait and representation. This paper also derives these indices and quantifies them from system parameters.
AB - This paper investigates a new approach to formulate performance indices of biometric system using information theoretic models. The performance indices proposed here (unlike conventionally used FAR, GAR, DET etc.) are scalable in estimating performance of large scale biometric system. This work proposes a framework for identification capacity of a biometric system, along with insights on number of cohort users, capacity enhancements from user specific statistics etc. While incorporating feature level information in a rate-distortion framework, we derive condition for optimal feature representation. Furthermore, employing entropy measures to distance (hamming) distribution of the encoded templates, this paper proposes an upper bound for false random correspondence probability. Our analysis concludes that capacity can be the performance index of a biometric system while individuality expressed in false random correspondence can be the performance index of the biometric trait and representation. This paper also derives these indices and quantifies them from system parameters.
KW - FRC (false random correspondence probability)
KW - Identification capacity
KW - Individuality
KW - Joint source-channel coding
UR - http://www.scopus.com/inward/record.url?scp=37849000505&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
SN - 9783540745488
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1043
EP - 1056
BT - Advances in Biometrics - International Conference, ICB 2007, Proceedings
T2 - 2007 International Conference on Advances in Biometrics, ICB 2007
Y2 - 27 August 2007 through 29 August 2007
ER -