Classification of Human Facial Expression:

This book describes a computer vision system fordetection and classification of 7 basic facialexpressions. Facial expressions are communicated bysubtle changes in one or more discrete features suchas tightening the lips, raising the eyebrows, openingand closing of eyes or certain combination of them,which can be identified through monitoring thechanges in muscles movements located around the aboveregions. In our research, an analytic facerepresentation consists of 15 feature points has beenused that identifies the principle muscle actions andprovides visual observation of the discrete featuresresponsible for each of the 7 basic emotions. Featurepoints from the region of mouth have been detected bysegmenting the lip contour applying a variationalformulation of the level set method. A multi-detectorapproach of facial feature point detection has beenutilized for identifying the points of interest fromthe region of eye, eyebrow and nose. Feature vectorscomposed of 15 features are then obtained from thesefeature points and used to train a SVM classifier sothat the system can classify facial expressions froman unknown face image with a certain level of accuracy.

Autorentext

Abu Sayeed Md. Sohail received Master's in Computer Science from Concordia University, Canada in August 2007. Presently he is working there as a Ph.D. researcher. He has over 20 research publications in the area of Computer Vision. His research interest includes Computer Analysis of Facial Expressions, Design of Multimodal Human-Computer Interface.

Klappentext
This book describes a computer vision system for detection and classification of 7 basic facial expressions. Facial expressions are communicated by subtle changes in one or more discrete features such as tightening the lips, raising the eyebrows, opening and closing of eyes or certain combination of them, which can be identified through monitoring the changes in muscles movements located around the above regions. In our research, an analytic face representation consists of 15 feature points has been used that identifies the principle muscle actions and provides visual observation of the discrete features responsible for each of the 7 basic emotions. Feature points from the region of mouth have been detected by segmenting the lip contour applying a variational formulation of the level set method. A multi-detector approach of facial feature point detection has been utilized for identifying the points of interest from the region of eye, eyebrow and nose. Feature vectors composed of 15 features are then obtained from these feature points and used to train a SVM classifier so that the system can classify facial expressions from an unknown face image with a certain level of accuracy.