Feature to feature matching for lbp based face recognition

Abstract

This paper presents a novel face recognition method called Local Binary Patterns with Feature to Feature Matching (LBP-FF). Contrary to other LBP approaches, we do not focus on the operator itself, however we would like to improve the matching procedure. The current LBP based approaches concatenate all feature vectors into one vector and then compare these large vectors. By contrast, our method compares the features separately. A sophisticated distance measure composed from two parts is used for face comparison. Chi square distance and histogram intersection metrics are utilized for vector distance computation. The proposed approach is evaluated on four face corpora: AT&T, FERET, AR and ČTK database. We experimentally show that our method significantly outperforms all compared state-of-the-art methods on all the databases. It is also worth of noting that the ČTK corpus is a novel face dataset composed of the images taken in real-world conditions and is freely available for research purposes at http://ufi.kiv.zcu.cz or upon request to the authors.