Paper Info
Title
Distance entropy as an information measure for binary biometric representation
Abstract
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.
Year
DOI
Venue
2012
10.1007/978-3-642-35136-5_40
CCBR
Keywords
Field
DocType
biometric template,new entropy measure,information measure,user distribution,shannon entropy,population distribution,relative entropy,binary representation,binary biometric representation,euclidean space,distance entropy,security
Population,Transfer entropy,Pattern recognition,Information diagram,Artificial intelligence,Hamming space,Biometrics,Entropy (information theory),Kullback–Leibler divergence,Mathematics,Binary number
Conference
Citations 
PageRank 
References 
0
0.34
14
Authors
3
Name
Order
Citations
PageRank
Yi Cheng Feng1302.55
Pong C. Yuen22625171.07
Meng-Hui Lim318822.66