Facial expression recognition method based on gabor wavelet features and fractional power polynomial kernel PCA

Authors:
Shuai-shi Liu;Yan-tao Tian
Affiliations:
School of Communication Engineering, Jilin University;School of Communication Engineering, Jilin University
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Year:
2010

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Abstract

The existing methods of facial expression recognition are always affected by different illumination and individual A facial expression recognition method based on local Gabor filter bank and fractional power polynomial kernel PCA is presented for this problem in this paper Local Gabor filter bank can overcome the disadvantage of the traditional Gabor filter bank, which needs a lot of time to extract Gabor feature vectors and the high-dimensional Gabor feature vectors are very redundant The KPCA algorithm is capable of deriving low dimensional features that incorporate higher order statistic In addition, SVM is used to classify the features Experimental results show that this method can reduce the influence of illumination effectively and yield better recognition accuracy with much fewer features.