A new radial basis function network classifier for holistic recognition of universal facial expressions

Abstract

According to psychologists there are six types of universal facial expressions namely, "Fear", "Surprise", "Anger", "Sad", ''Disgust'' and "Happy". Holistic recognition of these facial expressions from static images requires nonlinear classifiers capable of operating on noisy highdimensional feature spaces. Often Radial Basis Function networks (RBFN) are used for classification in these applications. Conventional RBF networks however, in spite of their capabilities in working with high-dimensional feature spaces, often fail to deliver satisfactory performance in these scenarios due to small training sample sets, noisy features anUor features not following the required class smcture. This paper presents an improved RBFN architecture that overcomes these problems through asymmetrical scaling of feature axes according to specific requirements of the class structure of the classification problemThe scaling factors are computed automatically from the available mining samples, without any explicit analysis of their multi-variate statistical properties. The proposed network yielded an overall recognition rate of over 92% for the 6 expression classes, and a smaller network sue compared to other types of RBFN classifiers.

Inverse Kinematic Solutions Using Artificial Neural Networks

“…ANN have been used in many general classification problems [13][14] [15]. Also there are many applications of neural network based solutions in the arena of facial expression recognition [16][18] [17] and speech signal processing [19][20] [21]. ANNs, try to mirror the brain functions in a computerized way by resorting to the learning mechanism as the basis of human behavior.…”