Illumination invariant face recognition

Authors:
Guangyi Chen;Sridhar Krishnan;Yongjia Zhao;Wenfang Xie
Affiliations:
Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada;Department of Computer and Electrical Engineering, Ryerson University, Toronto, Ontario, Canada;State Key Lab. of Virtual Reality Technology and Systems, Beihang University, Beijing, P.R. China;Department of Mechanical & Industrial Engineering, Concordia University, Montreal, Quebec, Canada
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Year:
2013

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Abstract

We present a novel method for face recognition by enhancing the quality of the input face images, which may be too dark due to different lighting conditions. We propose to extract the FFT features or the dual-tree complex wavelet (DTCWT)-FFT features from the enhanced face images and use the Support Vector Machine as a classifier. Our experiments show that our proposed methods compare favourably to the FFT features without image enhancement, and the methods in [1] and [10] for the Extended Yale Face Database B and the CMU-PIE face database.