文献类型：期刊文献

英文题名：Sparse tensor embedding based multispectral face recognition

作者：Zhao, Haitao[1];Sun, Shaoyuan[2]

机构：[1]E China Univ Sci & Technol, Automat Dept, Shanghai 200237, Peoples R China;[2]Donghua Univ, Automat Dept, Shanghai, Peoples R China

年份：2014

卷号：133

起止页码：427

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20141017422219);WOS:【SCI-EXPANDED(收录号:WOS:000334481400041)】；

基金：The authors would like to thank the anonymous reviewers and the financial support from the National Science Foundation of China (Project nos. 61375007 and 61072090) and Shanghai Pujiang Program (Project no. 1291402200).

语种：英文

外文关键词：Multispectral face recognition; Multibiometrics; Sparse tensor embedding

摘要：Face recognition using different imaging modalities has become an area of growing interest. A large number of multispectral face recognition algorithms/systems have been proposed in last decade. How to fuse features of different spectrum has still been a crucial problem for face recognition. To address this problem, we propose a sparse tensor embedding (STE) algorithm which represents a multispectral image as a third-order tensor. STE constructs sparse neighborhoods and the corresponding weights of the tensor. One advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. STE iteratively obtains one spectral space transformation matrix through preserving the sparse neighborhoods. Due to sparse representation, STE can not only keep the underlying spatial structure of multispectral images but also enhance robustness. The experiments on multispectral face databases, Equinox and PolyU-HSFD face databases, show that the performance of the proposed method outperform that of the state-of-the-art algorithms. (C) 2014 Elsevier B.V. All rights reserved.