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Abstract
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Article Information:
Comparison of Face Recognition Based on Global, Local and Component Classifiers using Multisensory Images
M. Ramkumar Prabhu, S. Rajkumar and A. Sivabalan
Corresponding Author: M. Ramkumar Prabhu
Submitted: November 17, 2010
Accepted: February 09, 2012
Published: June 15, 2012 |
Abstract:
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A feature selection technique along with an information fusion procedure for improving the
recognition accuracy of a visual and thermal image-based facial recognition system is presented in this study.
A novel modular Kernel Eigen spaces approach is developed and implemented on the phase congruency feature
maps extracted from the visual and thermal images individually. This study proposes a novel face recognition
method which exploits both global and local discriminative features. In this method, global features are
extracted from the whole face images by keeping the low-frequency coefficients of fourier transform, which
we believe encodes the holistic facial Information, such as facial contour. For local feature extraction, Gabor
wavelets are exploited considering their biological relevance. After that, to the global fourier features and each
local patch of Gabor features.
Key words: Ensemble classifier, face recognition, feature extraction, Fisher’s Linear Discriminant (FLD), image fusion, Kernel methods, phase congruency
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Cite this Reference:
M. Ramkumar Prabhu, S. Rajkumar and A. Sivabalan, . Comparison of Face Recognition Based on Global, Local and Component Classifiers using Multisensory Images. Research Journal of Applied Sciences, Engineering and Technology, (12): 1625-1628.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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