关键词: 单训练样本, 人脸识别, 稀疏变化字典, K邻域分块, 自动加权, 投票机制

Abstract: Face recognition with single training sample per person, due to the lack of inner-class information to predict the complex variations, results in the recognition accuracy of commonly used algorithms declined sharply. And the best solutions can be broadly divided into two categories, one is to generate virtual training samples from original samples, the other is through learning a sparse variation dictionary to predict the variations. To solve this challenging problem, this paper proposes an algorithm based on automatic weights to the K nearest patches. Because the training dictionary which just has one training sample per class can not predict facial complex features change, this paper imports the sparse variation dictionary to represent the complex facial features changes. Then this paper divides the test sample into some sub-blocks, and picks out the K nearest patches of each sub-block to form a virtual testing set and gives automatic weights to those patches in the classification. Finally, this paper uses an improved voting mechanism to get classification results of the original test sample. Extensive experiments on representative face databases AR, CMU Multi-PIE and ORL demonstrate that the proposed algorithm is much more effective than state-of-the-art algorithms in dealing with face recognition with single training sample per person.

Key words:  single training sample, face recognition, sparse variation dictionary, K nearest patches, automatic weight, voting mechanism