Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine

International Journal of Research and Engineering

View Publication Info
Field Value
Title Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine
Creator Al-Dabagh, Mustafa Zuhaer Nayef
Alhabib, Mustafa H. Mohammed
AL-Mukhtar, Firas H.
Description Although many methods have been implemented in the past, face recognition is still an active field of research especially after the current increased interest in security. In this paper, a face recognition system using Kernel Discriminant Analysis (KDA) and Support Vector Machine (SVM) with K-nearest neighbor (KNN) methods is presented. The kernel discriminates analysis is applied for extracting features from input images. Furthermore, SVM and KNN are employed to classify the face image based on the extracted features. This procedure is applied on each of Yale and ORL databases to evaluate the performance of the suggested system. The experimental results show that the system has a high recognition rate with accuracy up to 95.25% on the Yale database and 96% on the ORL, which are considered very good results comparing with other reported face recognition systems.
Publisher IJRE Publisher
Date 2018-04-06
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Identifier https://digital.ijre.org/index.php/int_j_res_eng/article/view/330
Source International Journal of Research and Engineering; Vol 5 No 3 (2018): March 2018 Edition; 335-338
Language eng
Relation https://digital.ijre.org/index.php/int_j_res_eng/article/view/330/298
Rights Copyright (c) 2018 Mustafa Zuhaer Nayef Al-Dabagh, Mustafa H. Mohammed Alhabib, Firas H. AL-Mukhtar

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us


Copyright © 2015-2018 Simon Fraser University Library