Human Face Expression Recognition Based on Feature Fusion

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Abstract:

The Gabor wavelet is the important technique widely used in the areas of images recognition such as human face expression, it extract the more important grain features for face expression effective, but it does not take into account the relative changes in the important characteristics of each location of the point features. Aiming at recognizing the information of human face expression, fuse the geometry feature based on angle changes at key parts on face expression, and then a radial basis function (RBF) neural network is designed as the classifier to perform recognition. The results of the experiment in the human face expression database indicate that the recognition rate by the feature fusion is obviously superior to that of traditional method.

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115-120

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April 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Yang Jian, Yang Jing-Yu. Why can LDA be performed in PCA transformed space[J]. Pattern Recognition, 2003, 36: 563-566.

DOI: 10.1016/s0031-3203(02)00048-1

Google Scholar

[2] Yang Jingyu , Jing Zhong , Guo Yuefei. Eff icient Discriminant Feature Extraction and Recognition of Face Images [J]. 2000, 24(3): 193-198.

Google Scholar

[3] Liu C J, WechslerH. A Garbor feature classifier for face recognition[ A]. Eighth IEEE International Conference on ComputerVision[ C]. 2001-02: 270- 275.

Google Scholar

[4] Zhao Hao, WU Xiao-jun . Facial Expression Recognition Based on Improved United Model [J]. Computer Engineering. 2010, 3, PP206-209.

Google Scholar

[5] Zhang Z , Lyons M, Schuster M, et al. Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron [A]. Third IEEE Conf Face and Gesture Recognition[C] . 1998, 454-459.

DOI: 10.1109/afgr.1998.670990

Google Scholar