|Title:||Face recognition using independent component analysis and support vector machines||Authors:||Déniz Suárez, Oscar
|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.||URI:||http://hdl.handle.net/10553/433||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|
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