Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Foundations of human computing: facial expression and emotion
Proceedings of the 8th international conference on Multimodal interfaces
Simultaneous Facial Action Tracking and Expression Recognition in the Presence of Head Motion
International Journal of Computer Vision
Recognising facial expressions in video sequences
Pattern Analysis & Applications
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Boosted multi-resolution spatiotemporal descriptors for facial expression recognition
Pattern Recognition Letters
Dynamics of facial expression extracted automatically from video
Image and Vision Computing
Automatic facial expression recognition using facial animation parameters and multistream HMMs
IEEE Transactions on Information Forensics and Security
Recognition of facial expressions and measurement of levels of interest from video
IEEE Transactions on Multimedia
Towards a dynamic expression recognition system under facial occlusion
Pattern Recognition Letters
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Automatic recognition of facial expression is a necessary step toward the design of more natural human-computer interaction systems. This work presents a user-independent approach for the recognition of facial expressions from image sequences. The faces are normalized in scale and rotation based on the eye centers' locations into tracks from which we extract features representing shape and motion. Classification and localization of the center of the expression in the video sequences are performed using a Hough transform voting method based on randomized forests. We tested our approach on two publicly available databases and achieved encouraging results comparable to the state of the art.