A Local Min-Max Binary Pattern Based Face Recognition Using Single Sample per Class

In this paper, we propose a new representation, called Local Min-Max Binary Pattern (LMin-MaxBP), and apply it to face recognition with single sample per class. The local appearance based methods have been successfully applied to face recognition and achieved state-of-the-art performance. The Local Binary Pattern (LBP) has been proved to be effective for image representation. The motivation for the LMin-MaxBP is to find texture information to cope with the variation due to facial expression and perspective changes as well as reducing the length of the feature vectors in LBP’s histogram to speed up the matching process. Experiments on Yale, ORL and Indian face datasets shows that the proposed approach improves the performance in the scenario of one training sample per person with significant facial expression and perspective variations with large rotation angle up to 180 θ .