Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/72743
Title: Face recognition using independent component analysis and support vector machines
Authors: Déniz Suárez,Oscar 
Castrillon, M. 
Hernández, M. 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Artifacts
Face recognition
Support vector machines
Independent component analysis
Issue Date: 2003
Journal: Pattern Recognition Letters 
Conference: 3rd International Conference on Audia and Video Based Biometric Person Authentication (AVBPA 2001)
Abstract: 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.
URI: http://hdl.handle.net/10553/72743
ISSN: 0167-8655
DOI: 10.1016/S0167-8655(03)00081-3
Source: Pattern Recognition Letters [ISSN 0167-8655], v. 24 (13), p. 2153-2157, (Septiembre 2003)
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