Face Recognition based on Gradient Gabor feature

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Title Face Recognition based on Gradient Gabor feature
Author Zhang, Baochang; Gao, Yongsheng; Qiao, Yu
Publication Title Proceedings of the 2008 IEEE International Conference on Image Processing (ICIP)
Editor Gang Qian
Year Published 2008
Place of publication USA
Publisher IEEE
Abstract In this paper, a novel Gradient Gabor (GGabor) filter is proposed to extract multi-scale and multi-orientation features to represent and classify faces. Gradient Gabor combines the derivative of Gaussian functions and the harmonic functions to capture the features in both spatial and frequency domains to deliver orientation and scale information. The spatial positions are combined into Gaussian derivatives which allows it to provide more stable information. An Efficient Kernel Fisher analysis method is proposed to find multiple subspaces based on both GGabor magnitude and phase features, which is a local kernel mapping method to capture the structure information in faces. Experiments on two face databases, FRGC Version 1 and FRGC Version 2, are conducted to compare the performances of the Gabor and GGabor features, which show that GGabor can also be a powerful tool to model faces, and the Efficient Kernel Fisher classifier can improve the efficiency of the original kernel fisher method.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1109/ICIP.2008.4712152
Copyright Statement Copyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ISBN 1-4244-1764-3
Conference name The IEEE International Conference on Image Processing (ICIP) 2008
Location San Diego, California, U.S.A
Date From 2008-10-12
Date To 2008-10-15
URI http://hdl.handle.net/10072/22702
Date Accessioned 2008-06-18
Date Available 2010-07-06T06:59:58Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Computer Vision; PRE2009-Pattern Recognition
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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