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Abstract
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Article Information:
Efficient Discriminate Component Analysis using Support Vector Machine Classifier on Invariant Pose and Illumination Face Images
R. Rajalakshmi and M.K. Jeyakumar
Corresponding Author: R. Rajalakshmi
Submitted: August 14, 2014
Accepted: October 11, 2014
Published: March 05, 2015 |
Abstract:
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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.
Key words: Face recognition, histogram equalization, LDA and PCA, , , ,
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Cite this Reference:
R. Rajalakshmi and M.K. Jeyakumar, . Efficient Discriminate Component Analysis using Support Vector Machine Classifier on Invariant Pose and Illumination Face Images. Research Journal of Applied Sciences, Engineering and Technology, (7): 491-499.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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