An efficient sparse representation based classification for undersampled face recognition

Zizhu Fan, Lipan Kang, Zuoyong Li

Abstract: The typical sparse representation for classification (SRC) can obtain desirable recognition result when the training samples in each class are sufficient. Nevertheless, if the training sample set is small scale, i.e., each class has a few training samples, even single sample, the traditional SRC cannot perform well. Although one of the variants of the traditional SRC, the extended SRC(ESRC), can effectively address the above small-scale training set (SSTS) problem, its computational efficiency is very low and consequently constrains the application of the ESRC algorithm. In order to improve the computational efficiency of the ESRC algorithm, we propose a new algorithm based on coordinate descent scheme in this work. Our proposed algorithm is referred as to the fast extended SRC (FESRC) algorithm. Experiments on popular face datasets show that the FESRC algorithm can obtain the high computational efficiency without significantly degrading the recognition results.