Ubiquitous single-sample face recognition under occlusion based on sparse representation with dual features

Face recognition has attracted numerous research interests as a promising biometrics with many distinct advantages. However there are inevitable gaps lying between face recognition in lab condition and ubiquitous face recognition application in real word, which mainly caused by various illumination condition, random occlusion, lack of sample images and etc. To combat the influence of these impact factors, a novel dual features based sparse representation classification algorithm is proposed. It contains illumination robust feature based dictionary learning and fused sparse representation with dual features. Firstly, an enhanced center-symmetric local binary pattern (ECSLBP) derived from conducting center symmetric encoding on the fused component images is presented for dictionary construction. Then, sparse representation with dual features including both ECSLBP and CSLBP is conducted. The final recognition is derived from the fusion of both classification results according to a novel fusion scheme. Numerous experiments results on both Extended Yale B database and the AR database show that the proposed algorithm exhibits distinguished discriminative ability and state-of-the-art recognition rate compared with other existing algorithms, especially for single sample face recognition under random partial occlusion.