Please use this identifier to cite or link to this item:
Title: Face recognition using independent component analysis and support vector machines
Authors: Déniz Suárez, Oscar
Castrillón-Santana, Modesto 
Hernández, M.
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Reconocimiento facial
Issue Date: 2001
Journal: Lecture Notes in Computer Science 
Conference: 3rd International Conference on Audio- 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 fare 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.
ISBN: 3-540-42216-1
ISSN: 0302-9743
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [ISSN 0302-9743], v. 2091 LNCS, p. 59-64, (Diciembre 2001)
Appears in Collections:Actas de congresos
Adobe PDF (260,8 kB)
Show full item record

Google ScholarTM




Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.