A Binarization Scheme for Face Recognition Based on Multi-Scale Block Local Binary Patterns

Local binary patterns (LBP) represent a well-established technique for reliable facial recognition. Multi-scale block (MB) LBP, which process average pixel values of block sub-regions instead of single pixels, have been proposed in order to achieve more robust feature vectors, which consist of histograms of (MB)-LBP values. In this work, we present a simple, yet effective, method to binarize histograms obtained from a MB-LBP face recognition system. The proposed method extracts compact binary feature vectors which allow for a rapid comparison and which are required for privacy enhanced biometric processing, when biometric template protection is enabled. On the publicly available FERET and Extended-Yale-B dataset, the presented scheme reveals only a negligible drop in biometric performance compared to the original MB-LBP system, while a substantial speed-up is achieved in the comparison stage.