Research Article | OPEN ACCESS
Efficient Discriminate Component Analysis using Support Vector Machine Classifier on Invariant Pose and Illumination Face Images
R. Rajalakshmi and M.K. Jeyakumar
Department of Computer Application, Noorul Islam University, Kumaracoil, Tamil Nadu, India
Research Journal of Applied Sciences, Engineering and Technology 2015 7:491-499
Received: August 14, 2014 | Accepted: October 11, 2014 | Published: March 05, 2015
Abstract
Face recognition is the process of categorizing a person in an image by evaluating with a known face image library. The pose and illumination variations are two main practical confronts for an automatic face recognition system. This study proposes a novel face recognition algorithm known as Efficient Discriminant Component Analysis (EDCA) for face recognition under varying poses and illumination conditions. This EDCA algorithm overcomes the high dimensionality problem in the feature space by extracting features from the low dimensional frequency band of the image. It combines the features of both LDA and PCA algorithms and these features are used in the training set and is classified using Support Vector Machine classifier. The experiments were performed on the CMU-PIE datasets. The experimental results show that the proposed algorithm produces a higher recognition rate than the existing LDA and PCA based face recognition techniques.
Keywords:
Face recognition, histogram equalization , LDA and PCA,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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