Face Recognition Based on Adaptive Sparse Coefficient
李洪均, 徐子能, Suen, Ching-yee · 东华大学学报：英文版 · 2016
Recently,robust sparse coding achieves high recognition rates on face recognition( FR),even when dealing with occluded images. However,robust sparse coding is that the coefficients are guaranteed global sparse when solving the sparse coefficients. In this paper,the coefficient vector is divided into multiple regions. Then,the elements in the object region are enabled to approximate global maximum by adding two constraint conditions( the maximal element of coefficient vector is in the object region; the sum of elements in the object region is the maximum value among all regions),which makes the distribution of sparse coefficient adapt to different classes of testing images. The efficacy of the proposed approach is verified on publicly available databases( i. e.,AR and Extended Yale B).Furthermore, the proposed method still can achieve a good performance when the training samples are limited.