期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2013
卷号：10
期号：2
出版社：IJCSI Press
摘要：Abstract: A great deal of research has shown that sparse representation based classification (SRC) is a powerful tool for face recognition. Sparse coding is an unsupervised learning algorithm that learns a succinct high-level representation of the inputs, given only unlabeled data; representing each input as a sparse linear combination of a set of basic functions, whereas the importance of sparsity is greatly affirmed in SRC and in abundant relevant research. Most researchers neglected the collaborative representation (CR) in SRC. In this paper, a modified and efficient approach for face recognition is proposed, based on combining two of the most successful local face representations, collaborative representation based classification and regularized least square (CRC_RLS) with bilateral filtering (BF). The combination of the two yield considerably better performance than either when implemented alone. Furthermore, experiments and their results show that the proposed method in this work outperforms several alternative methods.
