Abstract

By coding the input testing image as a sparse linear combi- nation of the training samples via l1 -norm minimization, sparse represen- tation based classification (SRC) has been recently successfully used for face recognition (FR). Particularly, by introducing an identity occlusion dictionary to sparsely code the occluded portions in face images, SRC can lead to robust FR results against occlusion. However, the large amount of atoms in the occlusion dictionary makes the sparse coding computa- tionally very expensive. In this paper, the image Gabor-features are used for SRC. The use of Gabor kernels makes the occlusion dictionary com- pressible, and a Gabor occlusion dictionary computing algorithm is then presented. The number of atoms is significantly reduced in the computed Gabor occlusion dictionary, which greatly reduces the computational cost in coding the occluded face images while improving greatly the SRC ac- curacy. Experiments on representative face databases with variations of lighting, expression, pose and occlusion demonstrated the effectiveness of the proposed Gabor-feature based SRC (GSRC) scheme.