A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
LAFTER: Lips and Face Real-Time Tracker
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
TemporalBoost for Event Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Audiovisual laughter detection based on temporal features
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Parametric correspondence and chamfer matching: two new techniques for image matching
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Automatic facial expression recognition using boosted discriminatory classifiers
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Probabilistic expression analysis on manifolds
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Facial expression recognition using tracked facial actions: Classifier performance analysis
Engineering Applications of Artificial Intelligence
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This paper introduces a novel approach to facial expression recognition in video sequences. Low cost contour features are introduced to effectively describe the salient features of the face. Temporalboost is used to build classifiers which allow temporal information to be utilized for more robust recognition. Weak classifiers are formed by assembling edge fragments with chamfer scores. Detection is efficient as weak classifiers are evaluated using an efficient look up to a chamfer image. An ensemble framework is presented with all-pairs binary classifiers. An error correcting support vector machine (SVM) is utilized for final classification. The results of this research is a 6 class classifier (joy, surprise, fear, sadness, anger and disgust ) with recognition results of up to 95%. Extensive experiments on the Cohn-kanade database illustrate that this approach is effective for facial exression analysis.