Thursday 23rd of November 2017
 

Face Recognition Using SVM Based on LDA


Anissa Bouzalmat, Jamal Kharroubi and Arsalane Zarghili

We present a method for face recognition that investigate the overall performance of linear ,polynomial and RBF kernel of SVM for classification based on global approach and used images having different expression variations, pose and complex backgrounds. In the first we reduce dimensional feature vector by LDA method , the result of vectors feature propagates to a set of SVM classifier, we trained SVM classifier with linear and non linear kernel for each dataset (face94, face96, grimaces)[1,2,3] in the database . Experiments demonstrate that use the LDA method combined with SVM classifier and the choice of a suitable kernel function with optimal parameters can produce high classification accuracy compared to KNN classifier on a variety of images on different Database.

Keywords: Face Recognition, SVM, LDA, PCA, KNN

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ABOUT THE AUTHORS

Anissa Bouzalmat
Anissa Bouzalmat is a PhD student at Sidi Mohammed Ben Abdellah University ,Laboratory Transmission and Processing Information(LTTI group) in Morocco.

Jamal Kharroubi
Jamal Kharroubi is a professor at Sidi Mohammed Ben Abdellah University ,Laboratory Transmission and Processing Information(LTTI group) in Morocco.

Arsalane Zarghili
Arsalane Zarghili is a professor at Sidi Mohammed Ben Abdellah University ,Laboratory Transmission and Processing Information(LTTI group) in Morocco.


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