关键词: 人脸识别, 协作表示, 小样本问题, 多尺度块协作表示, 局部结构

Abstract: Small sample size is one of the most challenging problems in face recognition due to the difficulty of sample collection in many real-world applications, and the performance of cooperation representation will be severely affected. Multi-scale patch based on collaborative recognition algorithm can integrate the classification results with different scales effectively, but the sub-block calculations are independent of each other in the classification framework, which ignore the structural relationship among the blocks. The local structure method divides the image into multiple local areas, in which overlapped blocks are distributed in the same linear subspace. The subspace can react the structure relationship of the blocks, and improve the robustness under the circumstance of small sample. This paper proposes Local Structure based Multi-Patch Collaborative Representation（LS_MPCRC）, and the experimental results on YaleB and AR face image databases indicate that the proposed method has a good recognition performance in dealing with small sample size problem.

Key words: face recognition, collaborative representation, small sample size problem, multi-scale patch based collaborative representation, local structure