Reducing the feature vector length in local binary pattern based face recognition

In this paper we propose a method for reducing the length of the feature vectors in the local binary pattern (LBP) based face recognition. This is done to speed up the matching of the feature vectors in real-time face recognition and detection systems. We define a new discrimination concept of the uniform local binary patterns called symmetry. Patterns are assigned different levels of symmetry based on the number of ones or zeros they contain. These symmetry levels are rotation invariant allowing a general discrimination methodology. Empirical studies on both human perception and LBP face recognition accuracy using the standard FERET database confirm that the concept of symmetry is an efficient discriminator.