Fast kernel sparse representation based classification for Undersampling problem in face recognition

We propose a fast kernel sparse representation based classification (SRC) for undersampling problem, i.e., each class has very few training samples, in face recognition. The proposed algorithm exploits a nonlinear mapping to map the data from the original input space into a high-dimensional feature space. Then, it performs very fast sparse representation and classification of samples in this space. Similar to the typical SRC methods, the proposed approach is based on the L1 norm minimization, whose direct solution can be very time-consuming. In order to improve the computational efficiency, our method uses the coordinate descent method in the feature space, which can avoid directly solving the L1 norm minimization problem, and significantly expedites the computational procedure. Compared with other SRC methods based on the L1 norm minimization, our proposed method achieves very high computational efficiency, without significantly degrading the classification performance. Several experiments on popular face databases demonstrate that our method is a promising efficient kernel SRC based method.