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
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
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Journal of Cognitive Neuroscience
SVM-based selection of colour space experts for face authentication
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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In this paper, the problem of fusing colour information at the feature level using kernel based feature extraction techniques is considered within the framework of a face verification system. A few statistical feature extraction algorithms including kernel based methods are reviewed first. An automatic parameter selection process is then applied to optimise the adopted kernel methods. In an extensive experimentation on intensity images of the XM2VTS database, we show that the optimised nonlinear kernel methods in general and the GDA algorithm in particular outperform the basic linear approaches. The experimentation is repeated for colour images by concatenating the R,G,B vectors. We demonstrate that by combining the colour information using the proposed method, the performance of the resulting decision making scheme considerably improves.