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Collaborative representation-based fuzzy discriminant analysis for Face recognition

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Abstract

In face recognition, the dimensionality reduction (DR) method is usually used to extract the discriminative features of the image. However, the performance is easily affected by varying facial poses, expressions and illumination. To solve this problem, a novel DR algorithm, namely collaborative representation-based fuzzy discriminant analysis (CRFDA), is proposed in this paper. In CRFDA, each training sample is firstly collaboratively represented by the overall training samples, and the fuzzy membership degrees of each sample are computed in terms of the representation coefficients. Secondly, the fuzzy means of different classes are computed using the membership degrees. Thirdly, the between-class and within-class scatter matrices are calculated to model the separability and compactness of samples, respectively. Finally, the feature extraction standard is improved by maximizing the ratio of fuzzy between-class scatter to fuzzy within-class scatter. A large number of experiments on publicly available facial datasets demonstrate the effectiveness of the proposed method.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (Grant No.11101216), the University Level Scientific Research Project of Nanjing Xiaozhuang University (Grant No. 2019NXY25) and the Training Objects of High-Level Talents of Jiangsu Province.

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Authors and Affiliations

  1. College of Information and Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, China

    Changwei Chen

  2. College of Computer and Information, Hohai University, Nanjing, 210098, China

    Changwei Chen & Xiaofeng Zhou

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  1. Changwei Chen
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  2. Xiaofeng Zhou
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Correspondence to Changwei Chen.

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Chen, C., Zhou, X. Collaborative representation-based fuzzy discriminant analysis for Face recognition. Vis Comput 38, 1383–1393 (2022). https://doi.org/10.1007/s00371-021-02325-w

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