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Extended minimum-squared error algorithm for robust face recognition via auxiliary mirror samples

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Abstract

The changes of face images with poses and polarized illuminations increase data uncertainty in face recognition. In fact, synthesized mirror samples can be recognized as representations of the left–right deflection of poses or illuminations of the face. Symmetrical face images generated from the original face images also provide more observations of the face which is useful for improving the accuracy of face recognition. In this paper, to the best of our knowledge, it is the first time that the well-known minimum squared error classification (MSEC) algorithm is used to perform face recognition on an extended face database using synthesized mirror training samples, which is titled as extended minimum squared error classification (EMSEC). By modifying the MSE classification rule, we append the mirror samples to the training set for gaining better classification performance. First, we merge original training samples and mirror samples synthesized from original training samples per subject as mixed training samples. Second, EMSEC algorithm exploits mixed training samples to obtain the projection matrix that can best transform the mixed training samples into predefined class labels. Third, the projection matrix is exploited to simultaneously obtain transform results of the test sample and its nearest neighbor from the mixed training sample set. Finally, we ultimately classify the test sample by combining the transform results of the test sample and the nearest neighbor. As an extension of MSEC, EMSEC reduces the uncertainty of the face observation by auxiliary mirror samples, so that it has better robustness classification performance than traditional MSEC. Experimental results on the ORL, GT, and FERET databases show that EMSEC has better generalization ability than traditional MSEC.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive advice. This work was supported by the National Science Foundation of China (Grant no. 61233011), the Natural Science Foundation of Jiangsu Province (Grant nos. BK2012700), the Foundation of Artificial Intelligence Key Laboratory of Sichuan Province (Grant no. 2012RZY02), the Open Project Program of the State Key Lab of CAD&CG of Zhejiang University (Grant no. A1418), the Foundation of Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Key Technologies R&D Program of Jiangsu Province (Grant no. BE2012031) and Science and Technology Planning Project of Wuxi City (Grant no. CYE11G).

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Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Changbin Shao, Xiaoning Song & Xiaojun Wu

  2. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, People’s Republic of China

    Changbin Shao, Xiaoning Song & Xibei Yang

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  1. Changbin Shao
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  2. Xiaoning Song
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  3. Xibei Yang
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  4. Xiaojun Wu
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Correspondence to Xiaoning Song.

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Communicated by V. Loia.

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Shao, C., Song, X., Yang, X. et al. Extended minimum-squared error algorithm for robust face recognition via auxiliary mirror samples. Soft Comput 20, 3177–3187 (2016). https://doi.org/10.1007/s00500-015-1692-7

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  • DOI: https://doi.org/10.1007/s00500-015-1692-7

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