The nature of statistical learning theory
The nature of statistical learning theory
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Development of Entertainment Robot System by Using a Person Detection Method
VSMM '01 Proceedings of the Seventh International Conference on Virtual Systems and Multimedia (VSMM'01)
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This paper proposes an automatic face detection system that combines two novel methods to achieve invariant face detection and a high discrimination between faces and distractors in static color images of complex scenes. The system applies Orthogonal Fourier-Mellin Moments (OFMMs), recently developed by one of the authors [1], to achieve fully translation-, scale- and in-plane rotation-invariant face detection. Support Vector Machines (SVMs), a binary classifier based on a novel statistical learning technique that has been developed in recent years by Vapnik [2], are applied for face/non-face classification. The face detection system first performs a skin color-based image segmentation by modeling the skin chrominance distribution for several different chrominance spaces. Feature extraction of each face candidate in the segmented images is then implemented by calculating a selected number of OFMMs. Finally, the OFMMs form the input vector to the SVMs. The comparative face detection performance of the SVMs and of a multilayer perceptron Neural Network (NN) is analyzed for a set of 100 test images. For all the chrominance spaces that are used, the application of SVMs to the OFMMs yields a higher detection performance than when applying the NN. Normalized chrominance spaces produce the best segmentation results, and subsequently the highest rate of detection of faces with a large variety of poses, of skin tones and against complex backgrounds. The combination of the OFMMs and of the SVMs, and of the skin color-based image segmentation using normalized chrominance spaces, constitutes a promising approach to achieve robustness in the task of face detection.