The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Face Recognition Using Gram-Schmidt Orthogonalization for LDA
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Redundant Class-Dependence Feature Analysis Based on Correlation Filters Using FRGC2.0 Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Journal of Cognitive Neuroscience
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Reliable face recognition using adaptive and robust correlation filters
Computer Vision and Image Understanding
Pattern Recognition
Improving face recognition by combination of natural and Gabor faces
Pattern Recognition Letters
Fusing individual algorithms and humans improves face recognition accuracy
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
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In this paper we propose a nonlinear correlation filter using the kernel trick, which can be used for redundant class-dependence feature analysis (CFA) to perform robust face recognition. This approach is evaluated using the Face Recognition Grand Challenge (FRGC) data set. The FRGC contains a large corpus of data and a set of challenging problems. The dataset is divided into training and validation partitions, with the standard still-image training partition consisting of 12,800 images, and the validation partition consisting of 16,028 controlled still images, 8,014 uncontrolled stills, and 4,007 3D scans. We have tested the proposed linear correlation filter and nonlinear correlation filter based CFA method on this FRGC2.0 data. The results show that the CFA method outperforms the baseline algorithm and the newly proposed kernel-based non-linear correlation filters perform even better than linear CFA filters.