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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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References

  1. Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst.2(1), 37–52 (1987)

    Article  Google Scholar 

  2. Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell.23(2), 228–233 (2001)

    Article  Google Scholar 

  3. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci.3(1), 71–86 (1991)

    Article  Google Scholar 

  4. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence19(7), 711–720 (1997)

  5. Ye, J., Li, Q.: A two-stage linear discriminant analysis via QR-decomposition. IEEE Trans. Pattern Anal. Mach. Intell.7(6), 929–941 (2005)

    Google Scholar 

  6. Wang, X., Tang, X.: Dual-space linear discriminant analysis for face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 564–569 (2004)

  7. Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data-with application to face recognition. Pattern Recogn.34(10), 2067–2070 (2001)

    Article  Google Scholar 

  8. Song, F., Zhang, D., Wang, J., Liu, H., Tao, Q.: A parameterized direct LDA and its application to face recognition. Neurocomputing71(1), 191–196 (2007)

  9. Li, H., Jiang, T., Zhang, K.: Efficient and robust feature extraction by maximum margin criterion. IEEE Trans. Neural Networks17(1), 157–165 (2006)

    Article  Google Scholar 

  10. Chen, L., Liao, H., Ko, M., Lin, J., Yu, G.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recogn.33(10), 1713–1726 (2000)

    Article  Google Scholar 

  11. Ye, J., Janardan, R., Li, Q.: Two-dimensional linear discriminant analysis. Adv. Neural Inf. Process. Syst., pp.1569–1576 (2004)

  12. He, X., Niyogi, P.: Locality preserving projections. Adv. Neural. Inf. Process. Syst.16(1), 153–160 (2004)

    Google Scholar 

  13. He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.: Face recognition using Laplacian faces. IEEE Trans. Pattern Anal. Mach. Intell.27(3), 328–340 (2005)

  14. Chen, H.T., Chang, H.W., Liu, T.L.: Local discriminant embedding and its variants. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol2, pp.846–853 (2005)

  15. Yan, S.C., Xu, D., Zhang, B.Y., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell29(1), 40–51 (2007)

  16. Huang, P., Gao, G.: Local similarity preserving projections for face recognition. Int. J. Electron. Commun.69(11), 1724–1732 (2015)

  17. Huang, P., Chen, C.K., Tang, Z.M., Yang, Z.J.: Discriminant similarity and variance preserving projection for feature extraction. Neurocomputing139, 180–188 (2014)

    Article  Google Scholar 

  18. . Huang, P, Tang, Z.M., Chen, C.K., Yang, Z.J.: Local maximal margin discriminant embedding for face recognition. J. Vis. Commun. Image Represent.25(2), 296–305 (2014)

  19. Huang, P., Chen, C., Tang, Z., Yang, Z.: Feature extraction using local structure preserving discriminant analysis. Neurocomputing140, 104–113 (2014)

    Article  Google Scholar 

  20. Huang, P., Li, T., Gao, G., Yang, G.: Feature extraction based on graph discriminant embedding and its applications to face recognition. Soft Comput. 23 (2018)

  21. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell.31(2), 210–227 (2008)

  22. Qiao, L., Chen, S., Tan, X.: Sparsity preserving projections with applications to face recognition. Pattern Recogn.43(1), 331–341 (2010)

    Article  Google Scholar 

  23. Gui, J., Sun, Z., Jia, W., Hu, R., Lei, Y., Ji, S.: Discriminant sparse neighborhood preserving embedding for face recognition. Pattern Recogn.45(8), 2884–2893 (2012)

    Article  Google Scholar 

  24. Yang, J., Chu, D., Zhang, L., Xu, Y., Yang, J.: Sparse representation classifier steered discriminative projection with applications to face recognition. IEEE Trans. Neural Netw. Learn. Syst.24(7), 1023–1035 (2013)

  25. Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition. In: IEEE Conference on Computer Vision, pp.471–478 (2011)

  26. Yang, W., Wang, Z., Sun, C.: A collaborative representation based projections method for feature extraction. Pattern Recogn.48(1), 20–27 (2015)

    Article  Google Scholar 

  27. Huang, P., Li, T., Gao, G., Yao, Y., Yang, G.: Collaborative representation based local discriminant projection for feature extraction. Digital Signal Processing76, 84–93 (2018)

    Article MathSciNet  Google Scholar 

  28. Kwak, K.C., Pedrycz, W.: Face recognition using a fuzzy fisherface classifier. Pattern Recogn.38(10), 1717–1732 (2005)

  29. Wan, M., Lai, Z., Yang, G., Yang, Z., Zhang, F., Zheng, H.: Local graph embedding based on maximum margin criterion via Fuzzy Set. Fuzzy Sets Syst.318, 120–131 (2017)

    Article MathSciNet  Google Scholar 

  30. Huang, P., Yang, Z.J., Chen, C.K.: Fuzzy local discriminant embedding for image feature extraction. Comput. Electric. Eng.46, 231–240 (2015)

  31. Huang, P., Gao, G., Qian, C., Yang, G., Yang, Z.: Fuzzy linear regression discriminant projection for face recognition. IEEE Access5, 4340–4049 (2017)

    Article  Google Scholar 

  32. Naseem, I., Togneri, R., Bennamoun, M.: Linear regression for face recognition. IEEE Trans. Pattern Anal. Mach. Intell.32(11), 2106–2112 (2010)

    Article  Google Scholar 

  33. Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: 2nd IEEE Workshop on Applications of Computer Vision, pp.138–142 (1994)

  34. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face recognition algorithm. IEEE Trans. Pattern Anal. Mach. Intell.22(10), 1090–1104 (2000)

  35. Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell.25(12), 1615–1618 (2003)

    Article  Google Scholar 

Download references

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 toChangwei Chen.

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