Human face recognition under occlusion using LBP and entropy weighted voting

In this paper a new block-based algorithm has been proposed to deal with facial occlusion when only one sample per person is available. A Local Binary Pattern (LBP) descriptor is applied on the image subblocks to extract distinctive texture features from those areas separately. Chi-Square is employed as histogram similarity metric in local classifiers corresponding to different image blocks. Finally, a weighted majority voting scheme is used for decision fusion. Local entropy is proposed to devote weights to classifiers results according to the block informative richness. This way, we can reduce the effect of blocks with appearance deformation on the final decision. Experimental results show the significantly high recognition accuracy of our method on the challenging AR face database compared to recent well-known approaches, without imposing computational complexity.