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Face recognition using AMVP and WSRC under variable illumination and pose

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Abstract

Face recognition with variable illumination and pose is an important and challenging task in computer vision. In order to solve the problem that the accuracy of face recognition reduces with illumination and pose changes, this paper proposes a method via AMVP (AWULBP_MHOG_VGG_PCA) features and WSRC. In the proposed method, we need to extract AWULBP_MHOG and VGG_PCA features, respectively. As for AWULBP_MHOG features, firstly, variable illumination is normalized for face images. And uniform local binary pattern (ULBP) and multiple histogram of oriented gradient (MHOG) features are extracted from each block, which are called ULBP_MHOG features. Then, we use information entropy to obtain adaptively weighted ULBP_MHOG (AWULBP_MHOG) features. As for VGG_PCA features, we use the pre-trained VGG-Face model to extract VGG features from original face images. And PCA is used to reduce the dimension of VGG features to obtain VGG_PCA features. Then, AWULBP_MHOG and VGG_PCA features are combined to form AMVP (AWULBP_MHOG_VGG_PCA) features. Finally, test face images can be classified using weighted sparse represent (WSRC). The comparison experiments with different blocks, classifiers and features have been conducted on the ORL, Yale, Yale B and CMU-PIE databases. Experimental results prove that our method can improve the accuracy effectively for illumination and pose variable face recognition.

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Acknowledgements

This work has been supported by National Natural Science Foundation of China (61203261), China Postdoctoral Science Foundation funded project (2012M521335), Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT (Nanjing University of Information Science & Technology, Grant No.: KXK1404), Research Fund of Guangxi Key Laboratory of Multi-source Information Mining & Security (MIMS16-02) and the Fundamental Research Funds of Shandong University (2015JC014 and 2017JC043). We would also like to thank the ORL database of faces captured by AT & T Cambridge Laboratory, the Yale face database and the Yale face database B provided by the Center for Computational Vision and Control at Yale University and the CMU-PIE database from Carnegie Mellon University, respectively.

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Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, 250061, China

    Kang Wang, Zhenxue Chen & Chengyun Liu

  2. Department of Electrical and Computer Engineering, University of Windsor, Windsor, N9B 3P4, Canada

    Q. M. Jonathan Wu

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  1. Kang Wang
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  2. Zhenxue Chen
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  3. Q. M. Jonathan Wu
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  4. Chengyun Liu
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Correspondence to Zhenxue Chen.

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Wang, K., Chen, Z., Wu, Q.M.J. et al. Face recognition using AMVP and WSRC under variable illumination and pose. Neural Comput & Applic 31, 3805–3818 (2019). https://doi.org/10.1007/s00521-017-3316-x

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  • DOI: https://doi.org/10.1007/s00521-017-3316-x

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