Binarization of spectral histogram models: An application to efficient biometric identification

Abstract

Feature extraction techniques such as local binary patterns (LBP) or binarized statistical image features (BSIF) are crucial components in a biometric recognition system. The vast majority of relevant approaches employs spectral histograms as feature representation, i.e. extracted biometric reference data consists of sequences of histograms. Transforming these histogram sequences to a binary representation in an accuracy-preserving manner would offer major advantages w.r.t. data storage and efficient comparison. We propose a generic binarization for spectral histogram models in conjunction with a Hamming distance-based comparator. The proposed binarization and comparison technique enables a compact storage and a fast comparison of biometric features at a negligible cost of biometric performance (accuracy). Further, we investigate a serial combination of the binary comparator and histogram model-based comparator in a biometric identification system. Experiments are carried out for two emerging biometric characteristics, i.e. palmprint and ear, confirming the soundness of the presented technique.