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.
This paper is supported by the Key Project of Science and Technology Development Plan for Jilin Province. (Grant No. 20071152).
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Liu, Ss., Tian, Yt. (2010). Facial Expression Recognition Method Based on Gabor Wavelet Features and Fractional Power Polynomial Kernel PCA. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_19
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DOI: https://doi.org/10.1007/978-3-642-13318-3_19
Publisher Name: Springer, Berlin, Heidelberg
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