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Robust large margin discriminant tangent analysis for face recognition

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Abstract

Fisher’s Linear Discriminant Analysis (LDA) has been recognized as a powerful technique for face recognition. However, it could be stranded in the non-Gaussian case. Nonparametric discriminant analysis (NDA) is a typical algorithm that extends LDA from Gaussian case to non-Gaussian case. However, NDA suffers from outliers and unbalance problems, which cause a biased estimation of the extra-class scatter information. To address these two problems, we propose a robust large margin discriminant tangent analysis method. A tangent subspace-based algorithm is first proposed to learn a subspace from a set of intra-class and extra-class samples which are distributed in a balanced way on the local manifold patch near each sample point, so that samples from the same class are clustered as close as possible and samples from different classes will be separated far away from the tangent center. Then each subspace is aligned to a global coordinate by tangent alignment. Finally, an outlier detection technique is further proposed to learn a more accurate decision boundary. Extensive experiments on challenging face recognition data set demonstrate the effectiveness and efficiency of the proposed method for face recognition. Compared to other nonparametric methods, the proposed one is more robust to outliers.

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Notes

  1. Open set domains assume that new classes may be encountered. In contrast, closed set domains assume that all classes of a domain have been known and can be used in training.

  2. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

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Acknowledgments

This work was supported in part by Research Foundation for the Doctoral Program of the Ministry of Education of China (Grant No. 20100041120009), 985 Project in Sun Yat-sen University (Grant No. 35000-3181305), and NSF-Guangdong (Grant No. U0835005).

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Authors and Affiliations

  1. School of Software Technology, Dalian University of Technology, 116620, Dalian, China

    Nanhai Yang & Xiukun Wang

  2. National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, 100190, Beijing, China

    Ran He

  3. School of Information Science and Technology, Sun Yat-sen University, 510275, Guangzhou, China

    Wei-Shi Zheng

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  1. Nanhai Yang
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  2. Ran He
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  3. Wei-Shi Zheng
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  4. Xiukun Wang
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Correspondence to Ran He.

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Yang, N., He, R., Zheng, WS. et al. Robust large margin discriminant tangent analysis for face recognition. Neural Comput & Applic 21, 269–279 (2012). https://doi.org/10.1007/s00521-011-0589-3

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