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Abstract

Automatic facial feature localization has been a long- standing 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 SVMparameters. 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.

Notes

Sponsor: U. S. Naval Research Laboratory

Grant ID: N00173- 07-C-2040

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Human Sensing, Face Group, Component Analysis

Associated Project(s): Facial Feature Detection and Deception Detection

Text Reference

Minh Hoai Nguyen, Joan Perez, and Fernando De la Torre Frade, "Facial Feature Detection with Optimal Pixel Reduction SVMs," 8th IEEE International Conference on Automatic Face and Gesture Recognition, September, 2008.

BibTeX Reference

@inproceedings{Nguyen_2008_6185,
   author = "Minh Hoai Nguyen and Joan Perez and Fernando {De la Torre Frade}",
   title = "Facial Feature Detection with Optimal Pixel Reduction SVMs",
   booktitle = "8th IEEE International Conference on Automatic Face and Gesture Recognition",
   month = "September",
   year = "2008",
}

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