Fusing hierarchical multi-scale local binary patterns and virtual mirror samples to perform face recognition

The horizontal axis-symmetrical nature of faces is useful and interesting, which has successfully applied in face detection. In general, images of faces are not strictly captured under a frontal and natural pose. It implies that extra training samples could be generated by means of the symmetry of the face. In this paper, we develop a framework that fuses virtual mirror synthesized training samples as bases and hierarchical multi-scale local binary patterns (LBP) features for classification. More specifically, in the first stage of proposed method, sampling uncertainty of the linear approximation model is alleviated effectively by constructing extra synthesized mirror training samples which generated from original images. Subsequently, in the second stage, features are extracted from the above dictionary using a hierarchical multi-scale LBP scheme. In the third stage, we combine the synthesized samples and original ones together to describe a test sample and simultaneously determine the relatively informative training samples by means of exploiting the reconstruction deviation of all the dictionary atoms. Besides, the introduced sparse coding process with weak l1 constraint has superior competitiveness that the accuracy has been improved, while the complexity has been fallen. Ultimately, the following step determines again a new decomposition coefficient of remaining samples, which makes final decision of the classification. Experimental results that are engaged in various benchmark face databases have demonstrated the effectiveness of our algorithm.