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 so...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.展开更多
基金National Natural Science Foundations of China(Nos.61171077,61401239)Natural Science Foundation of Jiangsu Province,China(No.BK20130393)+1 种基金the Science and Technology Program of Nantong,China(Nos.BK2014063,BK2014066)Natural Sciences and Engineering Research Council of Canada
文摘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.
