Facial Expression Classification Using RBF and Back-Propagation Neural Networks
R. Q. Feitosa, M. M. B. R. Vellasco, D. V. Andrade, S. A. R. S. Maffra, and D. T. Oliveira
Proceedings of the 4th World Multiconference on Systemics, Cybernetics and Informatics (SCI'2000) and the 6th International Conference on Information Systems Analysis and Synthesis (ISAS'2000), Orlando, USA, August 2000, pp. 73-77.

Abstract: This article presents a facial expressions classification experiment using neural networks. The classification system is based on attributes extracted from human faces images using the principal component analysis (PCA) technique. Well-framed images were used in order to simplify the face detection on the image . Two different models of neural networks have been applied as classifiers: back-propagation and RBF networks. In the experiments for performance evaluation the networks achieved a recognition rate equal to 71.8% and 73.2% respectively for Back Propagation and RBF, which is consistent with the best results reported in the literature for the same data base and testing paradigm. An analysis of the confusion matrix suggest the combination of both networks for a better performance.

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Last Updated on January 3, 2006