Recognition of Human Facial Expression in 3D Space

Abstract

Facial expression is one of the most powerful resources for people to coordinate conversation and communicate emotions and other mental, social, and physiological cues. Studying the facial expression is one successful way of studying the emotional state as the facial expression represents ones emotional state successfully. We propose a method to identify the facial expression represented by human face in this thesis. Facial expression is an important channel of nonverbal communication as well. If the facial expression can be recognized automatically then the results can be applied in many important areas like human computer interaction which can be used to enable the communication between human and computers in a more natural way. Facial expressions were studied mostly using 2D images or 2D video sequences. Recent advances in imaging technology and ever increasing computing power have opened up new areas of automatic facial expression recognition. We have proposed a method to recognize the facial expression by analyzing the 3D geometry of the human face. The geometry of the human face is studied in 3 dimensional spaces and the facial expression is identi ed. There are six kinds of universally recognized facial expressions: happiness, sadness, fear, anger, disgust, and surprise. The classi cation of the facial expressions is done using an Arti cial Neural Network.