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.
