Single sample face recognition based on multiple features and twice classification

Abstract

In order to improve the performance of face recognition with single sample effectively, a face recognition method based on multiple features and twice classification is proposed. For obtaining sufficient face information, facial multiple features combining differential excitation and Compound Local Binary Pattern (CLBP) on the asymmetric region are extracted. Elastic Matching (EM) has better robustness for pose. However, the computation complexity of the method is rather high. Classifying twice strategy is proposed to short the time of data processing. Experimental results on ORL database and FERET database show that the method is effective in getting better recognition rate and speed, also has a certain robustness to pose. © 2013 Springer-Verlag.