A Temporal Network of Support Vector Machine Classifiers for the Recognition of Visual Speech
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
On Selecting an Appropriate Colour Space for Skin Detection
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Applications of Support Vector Machines for Pattern Recognition: A Survey
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Recognizing Expressions by Direct Estimation of the Parameters of a Pixel Morphable Model
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic human face counting in digital color images
ISPRA'09 Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation
From facial expression to level of interest: a spatio-temporal approach
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Detecting Facial Expressions for Monitoring Patterns of Emotional Behavior
International Journal of Monitoring and Surveillance Technologies Research
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This paper describes a trainable system capable of tracking faces and facial features like eyes and nostrils and estimating basic mouth features such as degrees of openness and smile in real time. In developing this system, we have addressed the twin issues of image representation and algorithms for learning. We have used the invariance properties of image representations based on Haar wavelets to robustly capture various facial features. Similarly, unlike previous approaches this system is entirely trained using examples and does not rely on a priori (hand-crafted) models of facial features based on optical flow or facial musculature.The system works in several stages that begin with face detection, followed by localization of facial features and estimation of mouth parameters. Each of these stages is formulated as a problem in supervised learning from examples. We apply the new and robust technique of support vector machines (SVM) for classification in the stage of skin segmentation, face detection and eye detection. Estimation of mouth parameters is modeled as a regression from a sparse subset of coefficients (basis functions) of an over complete dictionary of Haar wavelets.