Emotion recognition based on contours of features

Abstract

Facial gesture recognition is one of the important components of natural human-machine interfaces; it may also be used in behavioural science, security systems and inClinical practice. Automatic analysis of facial gestures is rapidly becoming an area of intense interest in computer science and human-computer interaction design communities. However, the basic goal of this area of research – translating detected facial changes into a human-like description of shown facial expression – is yet to be achieved. One of the main impediments to achieving this aim is the fact that human interpretations of a facial expression differ depending upon whether the observed person is speaking or not. A first step in tackling this problem is to achieve automatic detection of facial gestures that are typical for speech articulation. Although humans recognise facial expressions virtually without effort or delay, reliable expression recognition by machine is still a challenge. The face expression recognition problem is challenging because different individuals display the same expression differently. This paper presents an overview of gesture recognition in real time using the concepts of correlations. Our approach to seizing this step in the research on automatic facial expression analysis. We consider the six universal emotional categories namely joy, anger, fear, disgust, sadness and surprise.The applications of gesture recognition are manifold, ranging from sign language through medicalrehabilitation to virtual reality. In this paper, various algorithms for gesture recognition have been investigated. Firststep in any gesture recognition process is face detection. We investigated algorithms like color segmentation,morphological Image Processing etc. for face detection, and Eigen faces for gesture recognition.