3D Facial Expression Recognition with Geometrically Localized Facial Features

Abstract

This paper describes a pose invariant three-dimensional (3D) facial expression recognition method using distance vectors retrieved from 3D distributions of facial feature points to classify universal facial expressions. Probabilistic Neural Network architecture is employed as a classifier to recognize the facial expressions from a distance vector obtained from 3D facial feature locations. Facial expressions such as anger, sadness, surprise, joy, disgust, fear and neutral are successfully recognized with an average recognition rate of 87.8%.