A user dependent multiresolution approach for biometric data


This paper focuses on the use of a user dependent multi-resolution approach based on local ternary pattern (LTP) in biometric verification. Following an extensive review of the literature on texture descriptors, several methods are compared on three well known biometric problems: palm verification, knuckle verification and face verification. We propose approaches for extracting a set of local ternary pattern bins separately from the training set of each user, then the Chi square distance is used to compare two templates. We compare our system with the standard multiresolution approach and with the novel hierarchical local binary patterns (HLBP). Extensive experiments conducted over the two well-known biometric characteristics (palmprint and knuckleprint) show the strength of our approach. When each user is given the related selected bins, a near 0 equal error rate is obtained. When the impostor steals the "selected bins" of the user that he claims to be, our approach slightly outperforms both the standard multi-resolution approach and HLBP.

Keywords:texture descriptors; multi-resolution approach; local binary patterns; local ternary pattern; palmprint verification; knuckleprint verification

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