TY - GEN

T1 - Distance entropy as an information measure for binary biometric representation

AU - Feng, Yi Cheng

AU - YUEN, Pong Chi

AU - LIM, Meng Hui

N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.

PY - 2012

Y1 - 2012

N2 - To uphold the security of individuals, analyzing the amount of information in binary biometric representation is highly essential. While Shannon entropy is a measure to quantify the expected value of information in the binary representation, it does not account the extent to which every binary representation could distinctively identify a person in a population. Hence, it does not appropriately quantify the hardness of obtaining a close approximation of the user's biometric template if one maliciously leverages the population distribution. To resolve this, relative entropy has been used to measure information of user distribution with reference to the population distribution. However, existing relative-entropy estimation techniques that are based on statistical methods in the Euclidean space cannot be directly extended to the Hamming space. Therefore, we put forward a new entropy measure known as distance entropy and its estimation technique to quantify the information in binary biometric representation more effectively with respect to the discrimination power of the binary representation.

AB - To uphold the security of individuals, analyzing the amount of information in binary biometric representation is highly essential. While Shannon entropy is a measure to quantify the expected value of information in the binary representation, it does not account the extent to which every binary representation could distinctively identify a person in a population. Hence, it does not appropriately quantify the hardness of obtaining a close approximation of the user's biometric template if one maliciously leverages the population distribution. To resolve this, relative entropy has been used to measure information of user distribution with reference to the population distribution. However, existing relative-entropy estimation techniques that are based on statistical methods in the Euclidean space cannot be directly extended to the Hamming space. Therefore, we put forward a new entropy measure known as distance entropy and its estimation technique to quantify the information in binary biometric representation more effectively with respect to the discrimination power of the binary representation.

KW - Binary biometric representation

KW - distance entropy

KW - security

UR - http://www.scopus.com/inward/record.url?scp=84871382958&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-35136-5_40

DO - 10.1007/978-3-642-35136-5_40

M3 - Conference proceeding

AN - SCOPUS:84871382958

SN - 9783642355059

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 332

EP - 339

BT - Biometric Recognition - 7th Chinese Conference, CCBR 2012, Proceedings

T2 - 7th Chinese Conference on Biometric Recognition, CCBR 2012

Y2 - 4 December 2012 through 5 December 2012

ER -