Reviews of Sparse Representation and Its Applications in Face Recognition
Xi Tang · 2014
Sparse representation is one of the hottest data representation methods which is thought to underlie the neural representations used by brain,and it has been developed sound theoretical foundation.In recent years,sparse representation based classification(SRC)has led to the interesting face recognition results and it looks for the sparsest representation of a query face image with respect to a dictionary composed of all the training images.However,the-norm regularized sparse representation is not stable and fails to incorporate the label information of training samples.Group sparse classification(GSC)extends SRC,according to label information,the training samples are grouped.GSC only selects a few groups to represent the query sample by using an-norm regularization.However,for a particular group,all the training samples are selected.A new classification method called weighted group sparse representation classification(WGSRC)to classify aquery image by minimizing the weighted mixed-norm(-norm)regularized reconstruction error with respect to training images is proposed.WGSRC gives each group a weight.We try to represent a test sample by training samples not only from the neighbors of it,but also from the highly relevant classes.