文章基本信息

标题：Lasso Regularized Gabor Shearlet Face Multivariate Sparse Function Approximation
本地全文：下载
作者：LI Dong-Rui
期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：8
页码：125-134
出版社：SERSC
摘要：In allusion to such problems in the traditional face recognition methods as poor recognition accuracy and dissatisfactory processing effect for directivity and anisotropic characteristic in face data, lasso regularized Gabor shearlet face multivariate sparse function approximation algorithm is proposed in this article. Firstly, Gabor improved shearlet algorithm is adopted at the level of the face-image biological signals for the sparse expansion representation of the face data characteristics, and meanwhile this algorithm is also adopted to extract the geometrical characteristics of the expansion face with directivity and anisotropic characteristic. Secondly, in order to balance the algorithm effect, lasso regularization theory is introduced therein to control and weigh the relation between the fidelity and the smoothness of the face data. Finally, the corresponding simulation experiment is carried out to compare the proposed algorithm and the existing algorithms in the standard test database in order to verify the advantages of the proposed algorithm in the aspect of face recognition accuracy and efficiency
关键词：Recognition accuracy; Shearlet; Face recognition; Smoothness; Sparse ;approximation