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2008
IEEE

Facial feature detection with optimal pixel reduction SVM

8 years 8 months ago
Facial feature detection with optimal pixel reduction SVM
Automatic facial feature localization has been a longstanding challenge in the field of computer vision for several decades. This can be explained by the large variation a face in an image can have due to factors such as position, facial expression, pose, illumination, and background clutter. Support Vector Machines (SVMs) have been a popular statistical tool for facial feature detection. Traditional SVM approaches to facial feature detection typically extract features from images (e.g. multiband filter, SIFT features) and learn the SVM parameters. Independently learning features and SVM parameters might result in a loss of information related to the classification process. This paper proposes an energy-based framework to jointly perform relevant feature weighting and SVM parameter learning. Preliminary experiments on standard face databases have shown significant improvement in speed with our approach.
Minh Hoai Nguyen, Joan Perez, Fernando De la Torre
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where FGR
Authors Minh Hoai Nguyen, Joan Perez, Fernando De la Torre
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