Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial event classification with task oriented dynamic Bayesian network
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
FACS-coding of facial expressions
CompSysTech '09 Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing
Spontaneous facial expression recognition: A robust metric learning approach
Pattern Recognition
| Hi-index | 0.00 |
Facial expressions play an important role in human nonverbal communication. They can be generated by activation and dilatation of facial muscles. In this paper we describe a system to recognize facial expressions automatically. Special areas in the face have been selected to extract features from the vector flow of visual muscle activity. To classify facial expressions Bayesian Networks have been used. The classifier has been trained and tested on video recordings from the Cohn Kanade database. It contains recordings from the six basic emotions as defined by Ekman. The model and results of testing are reported in the paper.