Single Sample Per Person Face Recognition Based on Sparse Representation with Extended Generic Set
Yi Ding, Lin Qi, Yun Tie, Chengwu Liang, Zizhe Wang · 2018
The single sample per person(SSPP) face recognition is one of the most essential problems of face recognition. Moreover, the testing samples are typically corrupted by nuisance variables such as expression, illumination and glasses. To address this problem, plenty of methods have been proposed to surmount the adverse effect of variances to testing samples in complex surroundings, but they are not robust. Therefor we proposed the Single sample per person face recognition based on sparse representation with extended generic set (SRGES). First, a set of general sample set were introduced, and the variation information of generic face samples set was extracted and extended to the single training sample set. Then, reconstruction error of testing sample is generated on training samples set by sparse representation model. Finally, the recognition is achieved depending on this sparse reconstruction error. The experimental results on the AR database, Extended Yale B database, CAS-PEAL database and LFW database displayed that the proposed algorithm is robust to variation feature for SSPP face recognition, and outperforms the state-of-art methods.