Human facial expression recognition using hybrid network of PCA and RBFN

Authors:
Daw-Tung Lin
Affiliations:
Department of Computer Science and Information Engineering, National Taipei University, Sanshia, Taipei County, Taiwan
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

In this paper, we propose a hybrid architecture combining radial basis function network (RBFN) and Principal Component Analysis (PCA) re-constructure model to perform facial expression recognition from static images. The resultant framework is a two stages coarse to fine discrimination model based on local features extracted from eyes and face images by applying PCA technique . 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 classify between seven prototypic facial expressions, such as neutral, joy, 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)”. As anticipated, the experimental results demonstrate the potential capabilities of the proposed approach.