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
Independent component analysis for identification of artifacts in magnetoencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Journal of Cognitive Neuroscience
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Eigenface vs. Spectroface: a comparison on the face recognition problems
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Eigenface vs. Spectroface: a comparison on the face recognition problems
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
A comparative study of automatic face verification algorithms on the BANCA database
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Modeling face appearance with nonlinear independent component analysis
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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Support Vector Machines (SVM) and Independent Component Analysis (ICA) are two powerful and relatively recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. ICA is a feature extraction technique which can be considered a generalization of Principal Component Analysis (PCA). ICA has been mainly used on the problem of blind signal separation. In this paper we combine these two techniques for the face recognition problem. Experiments were made on two different face databases, achieving very high recognition rates. As the results using the combination PCA/SVM were not very far from those obtained with ICA/SVM, our experiments suggest that SVMs are relatively insensitive to the representation space. Thus as the training time for ICA is much larger than that of PCA, this result indicates that the best practical combination is PCA with SVM.