关键词: 人脸识别, 字典扩展, l2-范数最小化

Abstract: This paper proposes an algorithm based on the l2-norm minimization which is much better than the l1-norm minimization. This algorithm is much more accurate on some database and efficient than the traditional sparse representation-based classification. The algorithm adds a feature dictionary into the training dictionary, which can increase the proportion of the feature information and raise the recognition rate. In a series of experiments, it can be found that the method is more accurate in the recognition rate and robust to both pixel corruption and block occlusion than other methods.

Key words: face recognition, dictionary expansion, l2-norm minimization