Design and Analysis of Face Recognition System based on VGG-Face-16 with Various Classifiers

Duaa Faris Abdlkader, M. F. Ghanim

Abstract


This research presents a face recognition system based on different classifiers that deals with various face positions. The proposed system involves the extraction of features through VGG-Face-16 deep neural network, which only extracts essential features of input images, leading to an improved recognition step and enhanced algorithm efficiency, while the recognition involves the Radial Basis Function in SVM classifier. The main contribution of this work is the implementation of face recognition by using Radial Basis Function in SVM classifier with VGG-face 16 and evaluate the performance of the system. Also, the system is designed and implemented later by using other classifiers; they are K-nearest neighbor (KNN) Classifiers, Logistic Regression (LR), Gradient Boosting (XGBoost), Decision Tree classifier (DT) and Naive Bayes Classifier (NB). The proposed algorithm was tested with the four face databases: AT&T, PINs Face, LFW and Real database. The database was divided into two groups: One contains a percentage of images that are used for training and the second contains a percentage of images (remainder) which was used for testing. The results show that the classification by radial basis function in SVM has the highest recognition rate in the case of using small, medium and morphometric databases; it was 100% in AT&T and Real database, while its efficiency appears to be lower when using large-size databases where as it is 96% in PINs database and 60.1% in LFW database.

Keywords


VGG-Face-16, SVM, KNN, LR, XGB, DT, NB



DOI: http://doi.org/10.11591/ijai.v11.i3.pp%25p

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