Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
Kernel-based nonlinear discriminant analysis for face recognition
Journal of Computer Science and Technology
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Carrying Status in Visual Surveillance
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image
IEEE Transactions on Image Processing
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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A face recognition method by using large and representative datasets is presented in this paper. The importance of research on face recognition is fueled by both its scientific challenges and its potential applications. In this contribution, we proposes several approaches to deal with some of the difficulties that one encounters when trying to recognize frontal faces in unconstrained domains and when only one sample per class is available to the learning system. It is possible for an automatic recognition system to compensate for imprecisely localized, partially expression variant faces even when only one single training sample per class is available. Finally, we have shown that the results of an appearance-based approach totally depend on the differences that exist between the facial expressions displayed on the learning and testing images.