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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Lin, D. T. (2006). Human facial expression recognition using hybrid network of PCA and RBFN. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4132 LNCS-II, pp. 624–633). Springer Verlag. https://doi.org/10.1007/11840930_65
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