Face recognition using Hog feature and group sparse coding

Standard sparsity concept mainly focuses on the sparsity of coefficient vector while other important characteristics are less considered. For example, given a structured dictionary, some structured patterns are more likely to occur than the others. In this paper, a face recognition method using group sparse coding is presented. Training samples of the same class are gathered to form a sub-dictionary and a group structured dictionary is obtained by concentrating sub-dictionaries of all classes. We decompose each testing sample as a product of the group structured dictionary and a group sparse coefficient vector. Finally, recognition is accomplished by evaluating which class of training samples leads to the minimum reconstruction error. For better invariance to illumination and expression changes, histogram of oriented gradients (Hog) feature is extracted to represent face image. Experimental results on benchmark face databases show the proposed method leads to higher recognition rates and shorter recognition time.