An Evaluation of Face Recognition By using Principal Component Analysis with Support Vector Machine
K. Rajakumari1, C. Nalini2
1K. Rajakumari, Research Scholar, Department of CSE, Bharath Institute of Higher Education and Research (BIHER), Chennai (Tamil Nadu), India.
2C. Nalini, Professor, Department of CSE, Bharath Institute of Higher Education and Research (BIHER), Chennai (Tamil Nadu), India.
Manuscript received on 23 April 2019 | Revised Manuscript received on 02 May 2019 | Manuscript Published on 07 May 2019 | PP: 114-116 | Volume-7 Issue-6S3 April 2019 | Retrieval Number: F1023376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Face Recognition concepts are used in many applications such as criminal identification, biometric, access & Security, payment and camera surveillance. The aim of the proposed work is focuseing on the image face which has to be correctly recoginised data using Principle Components of Analysis(PCA) with supprt vector Machine(SVM) techniques which extract the features and reduce dimensionality. There are ten different images of each of 40 distinct subjects. The PCA with SVM produce the result more accurate compare than other methods. This research work clearly shows it has more recognition rate and less error rate.
Keywords: PCA, Face Recognition, ORL Database, Eculidean Distance, SVM.
Scope of the Article: Pattern Recognition