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http://hdl.handle.net/10553/72743
Título: | 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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