Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/72743
Títulos: Face recognition using independent component analysis and support vector machines
Autores/as: Déniz Suárez,Oscar 
Castrillon, M. 
Hernández, M. 
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Artifacts
Face recognition
Support vector machines
Independent component analysis
Fecha de publicación: 2003
Publicación seriada: Pattern Recognition Letters 
Conferencia: 3rd International Conference on Audia and Video Based Biometric Person Authentication (AVBPA 2001)
Resumen: 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
Fuente: Pattern Recognition Letters [ISSN 0167-8655], v. 24 (13), p. 2153-2157, (Septiembre 2003)
Colección:Artículos
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