Selective Facial Expression Recognition Using fastICA

Xiao Hua Zhang; Zhi Fei Liu; Ya Jun Guo; Li Qiang Zhao

doi:10.4028/www.scientific.net/AMR.433-440.2755

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 433-440Selective Facial Expression Recognition Using...

Selective Facial Expression Recognition Using fastICA

Abstract:

This paper proposes a facial expression recognition approach based on the combination of fastICA method and neural network classifiers. First we get some special facial expression regions, including eyebrows, eyes and mouth, in which wavelet transform is done to reduce the dimension. Then the fastICA method is used to extract these three facial features. Finally, BP neural network classifier is adopted to recognize facial expression. Experimental on the JAFFE database results show that the method is effective for both dimension reduction and recognition performance in comparison with traditional PCA and ICA method. We have obtained recognition rates as high as 93.33% in categorizing the facial expressions neutral, anger, or sadness. The best average recognition rate achieves 90.48%.

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Periodical:

Advanced Materials Research (Volumes 433-440)

Pages:

2755-2761

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.433-440.2755

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Online since:

January 2012

Authors:

Xiao Hua Zhang, Zhi Fei Liu, Ya Jun Guo, Li Qiang Zhao

Keywords:

Facial Expression Recognition, Feature Extraction, ICA, Neural Classifier

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