The nature of statistical learning theory
The nature of statistical learning theory
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Handwritten Numerals Using Gabor Features
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Human Carrying Status in Visual Surveillance
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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A novel nonlinear feature extraction method based on the scatter difference criterion in hidden space is developed. The main idea is that the original input space is first mapped into a hidden space through a hidden function, which is still referred to as the kernel function in the proposed method, and, in this space, feature extraction is conducted using the difference of between-class scatter and within-class scatter as the discriminant criterion. Different from the existing kernel-based feature extraction methods, the kernel functions used in the proposed method are not required to satisfy Mercer's theorem so that they can be chosen from a wide range. What is more important is that, due to the adoption of the scatter difference as the discriminant criterion for feature extraction, the proposed method essentially avoids the small sample size problem usually encountered in kernel Fisher discriminant analysis. Finally, extensive experiments have been performed on a subset of the FERET face database and the CENPARMI handwritten digital database. The experimental results indicate that the proposed method outperforms traditional scatter difference discriminant analysis in recognition performance.