Comparative Study of PCA, ICA, LDA using SVM Classifier
Anissa Bouzalmat, Jamal Kharroubi, and Arsalane Zarghili
Department of Computer Science, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, Route d'Imouzzer B.P.2202 Fez, 30000 Morocco
Abstract—Feature representation and classification are two key steps for face recognition. We compared three automated methods for face recognition using different method for feature extraction: PCA (Principle Component Analysis), LDA (Linear Discriminate Analysis), ICA (Independent Component Analysis) and SVM (Support Vector Machine) were used for classification. The experiments were implemented on two face databases, The ATT Face Database [1] and the Indian Face Database (IFD) [2] with the combination of methods (PCA+ SVM), (ICA+SVM) and (LDA+SVM) showed that (LDA+SVM) method had a higher recognition rate than the other two methods for face recognition.
Index Terms—face recognition, SVM, LDA, PCA, ICA.
Cite: Anissa Bouzalmat, Jamal Kharroubi, and Arsalane Zarghili, "Comparative Study of PCA, ICA, LDA using SVM Classifier," Journal of Emerging Technologies in Web Intelligence, Vol. 6, No. 1, pp. 64-68, February 2014. doi:10.4304/jetwi.6.1.64-68
Index Terms—face recognition, SVM, LDA, PCA, ICA.
Cite: Anissa Bouzalmat, Jamal Kharroubi, and Arsalane Zarghili, "Comparative Study of PCA, ICA, LDA using SVM Classifier," Journal of Emerging Technologies in Web Intelligence, Vol. 6, No. 1, pp. 64-68, February 2014. doi:10.4304/jetwi.6.1.64-68
Array