Machine Learning
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Membership authentication in the dynamic group by face classification using SVM ensemble
Pattern Recognition Letters
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis with Tensor Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Learning from examples in the small sample case: face expression recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The problem of high dimensionality in face verification tasks has recently been simplified by the use of underlying spatial structures as proposed in the Two Dimensional Principal Component Analysis, the Two Dimensional Linear Discriminant Analysis and the Coupled Subspaces Analysis. Besides, the Small Sample Size problem that caused serious difficulties in traditional LDA has been overcome by the spatial approach 2DLDA. The application of these advances to facial verification techniques using different SVM schemes as classification algorithm is here shown. The experiments have been performed over a wide facial database (FRAV2D including 109 subjects), in which only one interest variable was changed in each experiment: illumination, pose, expression or occlusion. For training the SVMs, only two images per subject have been provided to fit in the small sample size problem.