Classwise Sparse and Collaborative Patch Representation for Face Recognition

Abstract

Sparse representation has shown its merits in solving some classification problems and delivered some impressive results in face recognition. However, the unsupervised optimization of the sparse representation may result in undesired classification outcome if the variations of the data population are not well represented by the training samples. In this paper, a method of class-wise sparse representation (CSR) is proposed to tackle the problems of the conventional sample-wise sparse representation and applied to face recognition. It seeks an optimum representation of the query image by minimizing the class-wise sparsity of the training data. To tackle the problem of the uncontrolled training data, this paper further proposes a collaborative patch (CP) framework, together with the proposed CSR, named CSR-CP. Different from the conventional patch-based methods that optimize each patch representation separately, the CSR-CP approach optimizes all patches together to seek a CP groupwise sparse representation by putting all patches of an image into a group. It alleviates the problem of losing discriminative information in the training data caused by the partition of the image into patches. Extensive experiments on several benchmark face databases demonstrate that the proposed CSR-CP significantly outperforms the sparse representation-related holistic and patch-based approaches.