Distance entropy as an information measure for binary biometric representation

Yi Cheng Feng*, Pong Chi YUEN, Meng Hui LIM

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationBiometric Recognition - 7th Chinese Conference, CCBR 2012, Proceedings
Number of pages8
Publication statusPublished - 2012
Event7th Chinese Conference on Biometric Recognition, CCBR 2012 - Guangzhou, China
Duration: 4 Dec 20125 Dec 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7701 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th Chinese Conference on Biometric Recognition, CCBR 2012

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Binary biometric representation
  • distance entropy
  • security


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