关键词: 字典学习, Gabor特征, 自适应加权, 遗忘函数, 人脸识别

Abstract: In the dictionary learning algorithms based on Fisher criterion, the selection of initial dictionary, and the construction of the objective function, seriously influence the effect of dictionary learning. In order to reduce the influence of initial dictionary, improve the ability of expression and discrimination. A face recognition algorithm is presented based on Gabor features and adaptive weighted Fisher criteria. Firstly, Gabor filter is used to extraction face feature, and use the Gabor facial features as the face training sets. Secondly, by adding the attenuation function, and adaptive weighting the training set, improve the effect of the Fisher criterion dictionary learning algorithm. Finally, the coefficient errors of the test sample coding, are used to identify the category. The experimental results on AR and Extend Yale database show that this algorithm is not only a very good image information extract algorithm, but also can effectively improve the accuracy rate of face recognition.

Key words: dictionary learning, Gabor features, adaptive weighting, forgotten function, face recognition