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
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://digital.ijre.org/index.php/int_j_res_eng/article/view/330
10.21276/ijre.2018.5.3.3
 
Source International Journal of Research and Engineering; Vol 5 No 3 (2018): March 2018 Edition; 335-338
2348-7860
2348-7852
 
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
http://creativecommons.org/licenses/by/4.0
 

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