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Enhancing sparsity via full rank decomposition for robust face recognition

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Abstract

In this paper, we propose a fast and robust face recognition method named enhancing sparsity via full rank decomposition. The proposed method first represents the test sample as a linear combination of the training data as the same as sparse representation, then make a full rank decomposition of the training data matrix. We obtain the generalized inverse of the training data matrix and then solve the general solution of the linear equation directly. For obtaining the optimum solution to represent the test sample, we use the least square method to solve it. We classify the test sample into the class which has the minimal reconstruction error. Our method can solve the optimum solution of the linear equation, and it is more suitable for face recognition than sparse representation classifier. The extensive experimental results on publicly available face databases demonstrate the effectiveness of the proposed method for face recognition.

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Acknowledgments

This article is partly supported by the Natural Science Foundation of China (Grant Nos. 61203376, 61375012, 61263032, and 61362031), the General Research Fund of Research Grants Council of Hong Kong (Project No. 531708), the China Postdoctoral Science Foundation under Project 2012M510958 and 2013T60370, the Guangdong Natural Science Foundation under Project S2012040007289, and Shenzhen Municipal Science and Technology Innovation Council (Nos. JC201005260122A, JCYJ201206 13153352732 and JCYJ20120613134843060, JCYJ20130329152024199).

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Authors and Affiliations

  1. Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

    Yuwu Lu, Jinrong Cui & Xiaozhao Fang

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  1. Yuwu Lu
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  2. Jinrong Cui
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  3. Xiaozhao Fang
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Correspondence to Yuwu Lu.

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Lu, Y., Cui, J. & Fang, X. Enhancing sparsity via full rank decomposition for robust face recognition. Neural Comput & Applic 25, 1043–1052 (2014). https://doi.org/10.1007/s00521-014-1582-4

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  • DOI: https://doi.org/10.1007/s00521-014-1582-4

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