Abstract—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.
Index Terms—Facial action coding system, facial expression recognition, hidden markov model (HMM), neural network (NN), principal component analysis (PCA).
Md. Nurul Ahad Tawhid is with the dept. of IIT, University of Dhaka, Bangladesh (e-mail: firstname.lastname@example.org).
MD. Nasir Uddin Laskar is with the dept. of CSE and IT, UITS, Dahaka, on study leave. He is now pursuing MS in Kyung Hee University (e-mail: email@example.com).
Md. Haider Ali is with the dep. of CSE, University of Dhaka (e-mail: firstname.lastname@example.org).
Cite:Nurul Ahad Tawhid, Nasir Uddin Laskar, and Haider Ali, "A Vision-based Facial Expression Recognition and Adaptation System from Video Stream," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 535-539, 2012.