Multimedia Tools and Applications

, Volume 77, Issue 7, pp 9055–9069 | Cite as

Gabor tensor based face recognition using the boosted nonparametric maximum margin criterion

Article
  • 57 Downloads

Abstract

This paper proposes a new face recognition method that combines the ensemble learning with the third-order Gabor tensor. In this method, the third-order Gabor tensor is used to replace the vectorial Gabor feature representation in order to keep high-dimensional adjacent structures in images. In order to avoid to fall into the curse of the dimensions due to the tensor, a multilinear principle component analysis (MPCA) algorithm is utilized to reduce the dimensions of the Gabor tensor. The obtained low-dimensional Gabor tensor features are selected in term of their discriminant ability to form a vectorial Gabor feature representation. It is embedded into a new sample selection scheme to construct a new classifier. Different from the traditional sample selection, the samples with high misclassification rate regardless of their class is used to train a set of diversity Nonparametric Maximum Margin Criterion (NMMC) learners and the scheme allows each class to have different numbers of samples. In construction of the classifier, multiple weak classifiers are first trained in terms of the K-NN criterion and then these weak classifiers are fused into a boosted classifier in terms of the confidence levels of individual weak classifiers. The proposed method inherits the merit of both the boosting technique and the Gabor wavelets. Experimental results on several benchmark face databases show that it attains better performance than the existing state-of-the-art methods.

Keywords

Face recognition Ensemble learning Gabor wavelets MPCA 

Notes

Acknowledgments

This research was supported in part by the National Natural Science Foundation of China (Grant No. 61101246) and the Fundamental Research Funds for the Central Universities (Grant No. JB150209).

References

  1. 1.
    Chen LF, Liao HYM, Ko MT, Lin JC, Yu GJ (2000) A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recogn 33(10):1713–1726CrossRefGoogle Scholar
  2. 2.
    Deypir M, Alizadeh S, Zoughi T, Boostani R (2011) Boosting a multi-linear classifier with application to visual lip reading. Expert Syst Appl 38(1):941–948CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Kearns MJ, Valiant LG (2003) The boosting approach to machine learning: An overview. Nonlinear Estimation and Classification. Springer, New York, pp 149–171Google Scholar
  5. 5.
    Liao P, Liu J, Wang M, Ma H, Zhang W (2012) Ensemble local fractional LDA for face recognition. Proceedings of IEEE International Conference on Computer Science and Automation Engineering (CSAE) 3:586–590CrossRefGoogle Scholar
  6. 6.
    Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476CrossRefGoogle Scholar
  7. 7.
    Lu J, Plataniotis KN, Venetsanopoulos AN, Li SZ (2006) Ensemble-based discriminant learning with boosting for face recognition. IEEE Trans Neural Netw 17(1):166–178CrossRefGoogle Scholar
  8. 8.
    Lu H, Plataniotis KN, Venetsanopoulos AN (2008) MPCA: multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19(1):18–39CrossRefGoogle Scholar
  9. 9.
    Lu H, Plataniotis KN, Venetsanopoulos AN (2009) Boosting discriminant learners for gait recognition using MPCA features. Journal on Image and Video Processing 2009(1):1–11Google Scholar
  10. 10.
    Olshausen BA (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–609CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Qiu X, Wu L (2005) Nonparametric maximum margin criterion for face recognition. Proceedings of IEEE international conference on image processing 2:II-918-21Google Scholar
  13. 13.
    Schapire RE, Freund Y, Bartlett P, Lee WS (1998) Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Stat 26(5):1651–1686MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Turk M, Pentland AP (1991) Face recognition using eigenfaces. Proceedings of IEEE computer society conference on computer vision and pattern recognition. CVPR 1991. doi: 10.1109/CVPR.1991.139758, pp 586–591
  15. 15.
    Wang X, Tang X (2004) Random sampling LDA for face recognition[C]. Computer Vision and Pattern Recognition, CVPR, Proceedings of the IEEE computer society conference on. IEEE, 2: II-259-II-265 Vol. 2Google Scholar
  16. 16.
    Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1):37–52CrossRefGoogle Scholar
  17. 17.
    Wu J, Nian X, Yang W, Sun C (2015) MPCA on Gabor tensor for face recognition. Proceedings of the 2015 Chinese Intelligent Automation Conference Lecture Notes in Electrical Engineering 336:421–429CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Yu H, Yang J (2001) A direct LDA algorithm for high-dimensional data—with application to face recognition. Pattern Recogn 34(10):2067–2070CrossRefMATHGoogle Scholar
  20. 20.
    Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv (CSUR) 35(4):399–458CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Electronic EngineeringXidian UniversityXi’anChina

Personalised recommendations