A Probabilistic Approach to Feature Selection for Multi-class Text Categorization
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Learning to generate novel views of objects for class recognition
Computer Vision and Image Understanding
Environment adaptive 3D object recognition and pose estimation by cognitive perception engine
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Multi-view face recognition with min-max modular SVMs
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Fast learning for statistical face detection
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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We propose a discriminative feature selection method utilizing support vector machines for the challenging task of multi-view face recognition. According to the statistical relationship between the two tasks, feature selection and multi-class classification, we integrate the two tasks into a single consistent framework and effectively realize the goal of discriminative feature selection. The classification process can be made faster without degrading the generalization performance through this discriminative feature selection method. On the UMIST multi-view face database, our experiments show that this discriminative feature selection method can speed up the multi-view face recognition process without degrading the correct rate and outperform the traditional kernel subspace methods.