C4.5: programs for machine learning
C4.5: programs for machine learning
Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Digital Picture Processing
Face Recognition Using Active Appearance Models
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
An introduction to variable and feature selection
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Haar Features for FACS AU Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Learning real-time object detectors: probabilistic generative approaches
Learning real-time object detectors: probabilistic generative approaches
A generative framework for real time object detection and classification
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Dynamics of facial expression extracted automatically from video
Image and Vision Computing
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Facial expression recognition is a challenging task. A facial expression is fonned by contracting or relaxing different facial muscles on human face which results in temporally deformed facial features like wide open mouth, raising eyebrows or etc. Such a system presents challenges. For instances, lighting condition is a very difficult problem to constraint and regulate. On the other hand, real-time processing is also a challenging problem since there are so many facial features to be extracted and processed and sometime conventional classifiers are not even effective to handle those features and then produce good classification perfonnance. This paper discusses the issues on how the advanced feature selection techniques together with good classifiers can playa vital important role of real-time facial expression recognition. The content of this paper is a way to open-up a discussion about building a real-time system to read and respond to the emotions of people from facial expressions.