Cloud basis function neural network:Amodified RBF network architecture for holistic facial expression recognition

Abstract

The paper presents novel modifications to radial basis functions (RBFs) and a neural network based classifier for holistic recognition of the six universal facial expressions from static images. The new basis functions, called cloud basis functions (CBFs) use a different feature weighting, derived to emphasize features relevant to class discrimination. Further, these basis functions are designed to have multiple boundary segments, rather than a single boundary as for RBFs. These new enhancements to the basis functions along with a suitable training algorithm allow the neural network to better learn the specific properties of the problem domain. The proposed classifiers have demonstrated superior performance compared to conventional RBF neural networks as well as several other types of holistic techniques used in conjunction with RBF neural networks. The CBF neural network based classifier yielded an accuracy of 96.1%, compared to 86.6%, the best accuracy obtained from all other conventional RBF neural network based classification schemes tested using the same database.