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

  • Mustafa Zuhaer Nayef Al-Dabagh Department of Computer Science, Knowledge University, Kurdistan Region, Iraq
  • Mustafa H. Mohammed Alhabib Department of Communications and Computer Engineering, Cihan University-Erbil, Kurdistan Region, Iraq
  • Firas H. AL-Mukhtar Department of Computer Science, Knowledge University, Kurdistan Region, Iraq


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.


Download data is not yet available.


[1] Xingang Liu, Lingyun Lu, Zhixin Shen, Kaixuan Lu, “A novel face recognition algorithm via weighted kernel sparse representation”, Future Generation Computer Systems, Vol.80, pp. 653-663, 2018,
[2] Hwang-Ki Min, Yuxi Hou, Sangwoo Park, Iickho Song, ”A computationally efficient scheme for feature extraction with kernel discriminant analysis”, Pattern Recognition, Vol. 50, pp. 45-55, 2016.
[3] G. Baudat and F. Anouar, "Generalized Discriminant Analysis Using a Kernel Approach," in Neural Computation, vol. 12, no. 10, pp. 2385-2404, Oct. 1 2000.
[4] T.M. Cover, and P.E. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory,vol. 13, pp.21-27, 1967.
[5] V.N. Vapnik, Statistical learning theory, John Wiley & Sons, New York, 1998.
[6] P. Yang, S. Shan, W. Gao, S. Li, and D. Zhang, Face recognition using Ada-Boosted Gabor features, In FGR ,pp.356-361,2004.
[7] W. Zhang, S. Shan, W. Gao, and X. Chen, Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition, in: Proceeding of International Conference on Computer Vision (ICCV), pp. 786-791,2005.
[8] Mustafa Zuhaer Nayef Al-Dabagh,“Face Recognition Using LBP, FLD and SVM with Single Training Sample Per Person”, in International Journal of Scientific & Engineering Research, Volume 5, Issue 5, May-2014.
[9] M. A. Tahir, E. Khan and A. A. Salem, "Medical text categorization using SEBLA and Kernel Discriminant Analysis," 2015 2nd World Symposium on Web Applications and Networking (WSWAN), Sousse, pp. 1-6,2015.
[10] Zhifeng Li and Xiaoou Tang, "Bayesian face recognition using support vector machine and face clustering," Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. II-374-II-380 Vol.2..2004.
[11] Mustafa Zuhaer AL-Dabagh, Dr. Firas H. AL-Mukhtar."Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine", International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)),vol.4,no. 3, pp.258-263,2017.
[12] K. N. N. Hlaing and A. K. Gopalakrishnan, "Myanmar paper currency recognition using GLCM and k-NN," 2016 Second Asian Conference on Defence Technology (ACDT), Chiang Mai,pp. 67-72,2016.
[13] P. N. Bellhumer, J. Hespanha, and D. Kriegman. (1997,July). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. Retrieved from http://cvc.yale.edu/projects/yalefaces/yalefaces.html.
[14] Samaria F., Harter A. (1994,December).Parameterisation of a Stochastic Model for Human Face Identification. Retrieved from http://www.cl.cam.ac.uklresearch/dtglattarchive/facedatabase.html
How to Cite
AL-DABAGH, Mustafa Zuhaer Nayef; ALHABIB, Mustafa H. Mohammed; AL-MUKHTAR, Firas H.. Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine. International Journal of Research and Engineering, [S.l.], v. 5, n. 3, p. 335-338, apr. 2018. ISSN 2348-7860. Available at: <https://digital.ijre.org/index.php/int_j_res_eng/article/view/330>. Date accessed: 13 aug. 2020. doi: https://doi.org/10.21276/ijre.2018.5.3.3.