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The aim of this paper is to develop a real time vision-based facial expression recognition and adaptation system for human-computer interaction. Major objective of this research is to detect face, to identify and recognize user's facial expression using face image in real time and to be able to adapt with new user's facial expression. It also works on mixed race expression detection. It is based upon the eigenface algorithm. Which a small set of feature vectors are used to describe the variation between expression images. It is also being able to adapt new expression image in real time. The proposed system makes major contribution in implementing facial expression recognition and adaptation in real time. The facial expression recognition task is divided into two parts: first part consists of automatic face detection from video stream and preprocessing, second part consists of a classification step that employs Principal Component Analysis (PCA) to classify the expression into one of five categories. The algorithm has been tested using both static and dynamic images. The average precision and recall rate achieved by the system is about 88% for person specific recognition.

A Vision-based Facial Expression Recognition and Adaptation System from Video Stream