Orientation Selectivity for Representation of Facial Expression Changes

This paper presents a method for representation of facial expression changes using orientation selectivity of Gabor wavelets on Adaptive Resonance Theory (ART) networks, which are unsupervised and self-organizing neural networks that contain a stability-plasticity tradeoff. The classification ability of ART is controlled by a parameter called the attentional vigilance parameter. However, the networks often produce inclusions or redundant categories. The proposed method produces suitable vigilance parameters according to classification granularity using orientation selectivity. Moreover, the method can represent the appearance and disappearance of facial expression changes to detect dynamic, local, and topological feature changes from whole facial images.