Detailed Sparse Recognition Based on Priori Sampling Constraints and Extended Dictionary

To solve the problem of pattern recognition with occlusion, we propose a detailed sparse recognition algorithm based on priori sampling constraints and extended dictionary. Considering the problem that the training images can not span the occlusion variation under testing conditions, the possible occlusion dictionary (sunglasses, scarves ) is constructed at first, then priori local sampling sub-modular sparse classify is used to estimate the possible occlusion of testing images; After that, according to the residual size, we exploit borda count to assign different votes for local sub-modular which is unobstructed. Thus, the sample information can be obtained; Secondly on the basis of the result of occlusion pattern, global sparse representation are performed to assign votes for the testing images by constructing the sample occlusion extended dictionary; Finally ,two classification results are fused to accomplish pattern discrimination in the environment of occlusion. Experimental results show that the proposed method not only can give an accurate pattern category, but also obtain the information of occlusion category which is a detailed recognition result.