Facial expressions classification with hierarchical radial basis function networks

Proposes a hierarchical model of a radial basis function network to classify and to recognize facial expressions. This approach utilizes principal component analysis as the feature extraction process from static images. It decomposes the acquired data into a small set of characteristic features. Using hierarchical networks of Gaussian radial basis functions, we differentiate the images in the feature space and fulfil the classification task. The objective of this research is to develop a more efficient system to discriminate between seven facial expressions (happiness, sadness, surprise, fear, anger, disgust and neutral). A constructive procedure is detailed and the system performance is evaluated. We achieved a correct classification rate above 98.4%, which is overwhelming distinguished compared to other approaches.