关键词: 单样本, 结构化稀疏, 类内变化字典, 联合表示

Abstract: Single sample per person makes face recognition much more difficult. According to the fact that facial variations in same category should have similar coding coefficients, the proposed variance dictionary learning method with structured sparsity can effectively represent facial variance. Considering that all local regions from same person have same class label, query image is represented by gallery images patches and variance dictionary patches. Structured sparsity constraints are imposed on the reconstruction coefficients to automatically select corresponding class dictionary so that query image can be well represented. The proposed method can harvest the advantage of both local methods and holistic methods, and performs well compared with the existing solutions to the single sample problem on the AR, Extended Yale B, CMU-PIE datasets.

Key words: single sample per person, structured sparsity, intra-class variant dictionary, joint representation