Elements of information theory
Elements of information theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cancellable biometrics and annotations on BioHash
Pattern Recognition
Towards a measure of biometric feature information
Pattern Analysis & Applications
A hybrid biometric cryptosystem for securing fingerprint minutiae templates
Pattern Recognition Letters
A hybrid approach for generating secure and discriminating face template
IEEE Transactions on Information Forensics and Security
How to Measure Biometric Information?
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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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.