关键词: 稀疏编码, 稀疏表示, GSVGDL字典学习, Gabor特征, 人脸识别

Abstract: Dictionary learning in sparse coding plays an important role on image recognition based on sparse representation. Considering that Gabor feature is robust to variations of expression, illumination and pose. Therefore, a face recognition algorithm via sparse representation is proposed based on Gabor feature and Support Vector Guided Dictionary Learning（GSVGDL）. At first the image Gabor features are extractes and used as the augmented Gabor feature matrix to construct the initial dictionary. The dictionary learning model combines the reconstruction error with the discrimination term and the regularization term, and formulates the discrimination term as the weighted summation of the squared distances between all pairs of coding vectors. Then a structural dictionary and linear classifier is learned simultaneously by the dictionary learning, which the learned dictionary atoms are corresponded to the class labels. GSVGDL can adaptively assign different weights to different pairs of coding vectors and enhance the discrimination of the dictionary. Experiment results show that the proposed method has good recognition accuracy and higher recognition efficiency．

Key words: sparse coding, sparse representation, Gabor feature and Support Vector Guided Dictionary Learning（GSVGDL）, Gabor feature, face recognition