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.
2016 International Conference of the Biometrics Special Interest Group (BIOSIG)