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Face recognition based on subset selection via metric learning on manifold

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Abstract

With the development of face recognition using sparse representation based classification (SRC), many relevant methods have been proposed and investigated. However, when the dictionary is large and the representation is sparse, only a small proportion of the elements contributes to the l 1-minimization. Under this observation, several approaches have been developed to carry out an efficient element selection procedure before SRC. In this paper, we employ a metric learning approach which helps find the active elements correctly by taking into account the interclass/intraclass relationship and manifold structure of face images. After the metric has been learned, a neighborhood graph is constructed in the projected space. A fast marching algorithm is used to rapidly select the subset from the graph, and SRC is implemented for classification. Experimental results show that our method achieves promising performance and significant efficiency enhancement.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shenyang University of Technology, Shenyang, 110870, China

    Hong Shao, Shuang Chen & Wen-cheng Cui

  2. The University of Texas Health Science Center at Houston, Houston, 77030, USA

    Jie-yi Zhao

  3. Schulich School of Engineering, University of Calgary, Calgary, T2N 1N4, Canada

    Tian-shu Yu

Authors
  1. Hong Shao
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  2. Shuang Chen
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  3. Jie-yi Zhao
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  4. Wen-cheng Cui
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  5. Tian-shu Yu
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Corresponding author

Correspondence to Shuang Chen.

Additional information

Project supported by the Natural Science Foundation of Liaoning Province, China (No. 201202162)

ORCID: Shuang CHEN, http://orcid.org/0000-0001-7441-4749

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Shao, H., Chen, S., Zhao, Jy. et al. Face recognition based on subset selection via metric learning on manifold. Frontiers Inf Technol Electronic Eng 16, 1046–1058 (2015). https://doi.org/10.1631/FITEE.1500085

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  • DOI: https://doi.org/10.1631/FITEE.1500085

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