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Sparse tensor embedding based multispectral face recognition
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DOI：10.1016/j.neucom.2013.12.019.png)
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Abstract
En 中文
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.
Keywords:
Multispectral face recognition
Multibiometrics
Sparse tensor embedding
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6.5
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