The nature of statistical learning theory
The nature of statistical learning theory
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Cascaded Classification of Gender and Facial Expression using Active Appearance Models
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Nonlinear Shape and Appearance Models for Facial Expression Analysis and Synthesis
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A Real-Time Facial Expression Recognition using the STAAM
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A model based method for automatic facial expression recognition
ECML'05 Proceedings of the 16th European conference on Machine Learning
Effective Emotional Classification Combining Facial Classifiers and User Assessment
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
A review of active appearance models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Portable real time emotion detection system for the disabled
Expert Systems with Applications: An International Journal
Facial expression recognition for learning status analysis
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: users and applications - Volume Part IV
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The paper highlights the performance of video sequence-oriented facial expression recognition using Active Appearance Model -- AAM, in a comparison with the analysis based on still pictures. The AAM is used to extract relevant information regarding the shapes of the faces to be analyzed. Specific key points from a Facial Characteristic Point - FCP model are used to derive the set of features. These features are used for the classification of the expressions of a new face sample into the prototypic emotions. The classification method uses Support Vector Machines.