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Journal of Information Science and Engineering, Vol. 22 No. 5, pp. 1033-1046 (September 2006)

Facial Expression Classification Using PCA and Hierarchical Radial Basis Function Network*

Daw-Tung Lin
Department of Computer Science and Information Engineering
National Taipei University
Sanshia, 237 Taiwan

Intelligent human-computer interaction (HCI) integrates versatile tools such as perceptual recognition, machine learning, affective computing, and emotion cognition to enhance the ways humans interact with computers. Facial expression analysis is one of the essential medium of behavior interpretation and emotion modeling. In this paper, we modify and develop a reconstruction method utilizing Principal Component Analysis (PCA) to perform facial expression recognition. A framework of hierarchical radial basis function network (HRBFN) is further proposed to classify facial expressions based on local features extraction by PCA technique from lips and eyes images. It decomposes the acquired data into a small set of characteristic features. The objective of this research is to develop a more efficient approach to discriminate between seven prototypic facial expressions, such as neutral, smile, anger, surprise, fear, disgust, and sadness. A constructive procedure is detailed and the system performance is evaluated on a public database "Japanese Females Facial Expression (JAFFE)." We conclude that local images of lips and eyes can be treated as cues for facial expression. As anticipated, the experimental results demonstrate the potential capabilities of the proposed approach.

Keywords: intelligent human-computer interaction, facial expression classification, hierarchical radial basis function network, principal component analysis, local features

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Received August 16, 2005; accepted January 17, 2006.
Communicated by Jhing-Fa Wang, Pau-Choo Chung and Mark Billinghurst.
*This work was supported in part by the National Science Council of Taiwan, R.O.C., under grants No. NSC 88-2213-E216-010 and No. NSC 89-2213-E216-016.
*The preliminary content of this paper has been presented in "International Conference on Neural Information Processing," Perth, Australia, November 1999. Acknowledgement also due to Mr. Der-Chen Pan at the National Taipei University for his help in performing simulations. The author would like to thank Mr. Ming- Shon Chen at Ulead System Inc., Taipei, Taiwan, for his early work and assistance in this research.