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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 597245, 10 pages
Research Article

Robust Tensor Preserving Projection for Multispectral Face Recognition

1Automation Department, Donghua University, Shanghai 201620, China
2Automation Department, East China University of Science and Technology, Shanghai 200237, China
3School of Aeronautics and Astronautics, Shanghai Jiaotong University, Shanghai 200240, China

Received 14 April 2014; Accepted 21 July 2014; Published 12 August 2014

Academic Editor: Haipeng Peng

Copyright © 2014 Shaoyuan Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Multiple imaging modalities based face recognition has become a hot research topic. A great number of multispectral face recognition algorithms/systems have been designed in the last decade. How to extract features of different spectrum has still been an important issue for face recognition. To address this problem, we propose a robust tensor preserving projection (RTPP) algorithm which represents a multispectral image as a third-order tensor. RTPP constructs sparse neighborhoods and then computes weights of the tensor. RTPP iteratively obtains one spectral space transformation matrix through preserving the sparse neighborhoods. Due to sparse representation, RTPP can not only keep the underlying spatial structure of multispectral images but also enhance robustness. The experiments on both Equinox and DHUFO face databases show that the performance of the proposed method is better than those of related algorithms.