Sparse representation based classification has attracted much attention due to its robustness in the fields of biometrics, such as face recognition, palm-print recognition. SRC first constructs the linear representation model for the test sample, and then it classifies the test sample by comparing its coding error of each class. The coding error essentially can be viewed as the distance from sample to the subspace spanned by the samples from specific class. Therefore, the key to improve the classification performance of SRC is to enhance the subspace separability of these subspaces. In this paper, we introduce the difference subspace analysis into SRC, and propose difference subspace based SRC (DSSRC) for face recognition. Different from traditional dictionary learning based SRC methods, DSSRC focuses on maximizing the discriminability for the classes rather than the representation ability for the samples. Extensive experiments on the well-known image datasets demonstrate that the proposed DSSRC method is effective for face recognition.
['Qi Zhu', 'Qingxiang Feng', 'Jiashuang Huang', 'Dan Zhang']
Sparse representation classification based on difference subspace