Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Fully Automatic Facial Action Recognition in Spontaneous Behavior
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A Comprehensive Empirical Study on Linear Subspace Methods for Facial Expression Analysis
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
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
A generative framework for real time object detection and classification
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Automatic facial expression recognition using boosted discriminatory classifiers
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Recognizing facial expression using particle filter based feature points tracker
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
A discriminative feature space for detecting and recognizing faces
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning from examples in the small sample case: face expression recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper proposes an automatic facial expression recognition system, which uses new methods in both face detection and feature extraction. In this system, considering that facial expressions are related to a small set of muscles and limited ranges of motions, the facial expressions are recognized by these changes in video sequences. First, the differences between neutral and emotional states are detected. Faces can be automatically located from changing facial organs. Then, LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts. At last, SVM with polynomial kernel is used to classify expressions. The method is evaluated on JAFFE and MMI databases. The performances are better than other automatic or manual annotated systems.