Feature-Point Tracking by Optical Flow Discriminates Subtle Differences in Facial Expression
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric models for facial features segmentation
Signal Processing
Accurate and quasi-automatic lip tracking
IEEE Transactions on Circuits and Systems for Video Technology
EURASIP Journal on Applied Signal Processing
International Journal of Approximate Reasoning
Real-Time Facial Expression Recognition for Natural Interaction
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Effective Emotional Classification Combining Facial Classifiers and User Assessment
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Emotional facial expression classification for multimodal user interfaces
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Pain monitoring: A dynamic and context-sensitive system
Pattern Recognition
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This paper presents a system for classifying facial expressions based on a data fusion process relying on the Belief Theory (BeT). Four expressions are considered: joy, surprise, disgust as well as neutral. The proposed system is able to take into account intrinsic doubt about emotion in the recognition process and to handle the fact that each person has his/her own maximal intensity of displaying a particular facial expression. To demonstrate the suitability of our approach for facial expression classification, we compare it with two other standard approaches: the Bayesian Theory (BaT) and the Hidden Markov Models (HMM). The three classification systems use characteristic distances measuring the deformations of facial skeletons. These skeletons result from a contour segmentation of facial permanent features (mouth, eyes and eyebrows). The performances of the classification systems are tested on the Hammal-Caplier database [1] and it is shown that the BeT classifier outperforms both the BaT and HMM classifiers for the considered application.