Face Recognition Based on Joint Bi-Sparse Representation and Sample Extended Difference Template
Yang Bai · Signal Processing · 2012
In the face recognition,data in each category lie in multiple low-dimensional subspaces of a high-dimensional space respectively.Because the structure information plays a certain support role,we apply the block-structured sparse representation for face recognition.Considering the problem that the training images can not span the facial variation under testing conditions,a novel recognition method of joint bi-sparse representation based sample extended difference template is proposed,which applies an extended difference template to represent the possible variation between the training and testing images.These intra-category variation can be shared by other categories.In other words,the intra-category variation of any category can be represented as the atomic sparse linear combination.So the recognition problem is converted into finding a joint bi-sparse representation of the block-structured sparse representation and atomic sparse representation in the training sample space and extended difference template space.Experimental results on AR and Extended Yale B databases show that the proposed method has better effectiveness and robustness under variable expressions,illuminations and disguises.