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

Abstract

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.

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References

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Published
2018-04-06
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: 19 nov. 2019. doi: https://doi.org/10.21276/ijre.2018.5.3.3.