Coupled principal component analysis based face recognition in heterogeneous sensor networks

In this paper, we construct heterogeneous sensor networks (HSN) for face recognition and propose a novel approach named coupled principal component analysis (CPCA) that uses a feature-based representation for heterogeneous face images. We first employ local binary patterns (LBP) to capture the local...

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Bibliographic Details
Published in:Signal processing 2016-09, Vol.126, p.134-140
Main Authors: Zhang, Zhong, Liu, Shuang
Format: Article
Language:English
Subjects:
Face recognition
Heterogeneous sensor network
Joining
Networks
Principal component analysis
Representations
Sensors
State of the art
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Summary:In this paper, we construct heterogeneous sensor networks (HSN) for face recognition and propose a novel approach named coupled principal component analysis (CPCA) that uses a feature-based representation for heterogeneous face images. We first employ local binary patterns (LBP) to capture the local structure of face images, and then propose CPCA to obtain the global face information. The proposed CPCA could incorporate the information between heterogeneous feature spaces, and therefore it reduces the gap between face images captured from heterogeneous sensors in HSN. Finally, the spare representation is utilized for matching heterogeneous face images. The experimental results demonstrate that the proposed approach achieves better performance than the state-of-the-art approaches. •A framework for combining local and global features in HSN is proposed.•We propose CPCA for face recognition in HSN.•The CPCA could reduce the gap between heterogeneous sensor nodes.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2015.08.013