Gabor tensor based face recognition using the boosted nonparametric maximum margin criterion
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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.
KeywordsFace recognition Ensemble learning Gabor wavelets MPCA
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).
- 3.FERET database. From http://mloss.org/software/view/416/
- 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
- 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
- 11.ORL Database . From http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html
- 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
- 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.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
- 18.XM2VTS database. From http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/