Gabor Feature Constrained Statistical Model for Efficient Landmark Localization and Face Recognition

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Title Gabor Feature Constrained Statistical Model for Efficient Landmark Localization and Face Recognition
Author Zhao, Sanqiang; Gao, Yongsheng; Zhang, Baochang
Journal Name Pattern Recognition Letters
Editor T.K. Ho; G. Sanniti di Baja
Year Published 2009
Place of publication Netherlands
Publisher Elsevier B.V.
Abstract Feature extraction and classification using Gabor wavelets have proven to be successful in computer vision and pattern recognition. Gabor feature-based Elastic Bunch Graph Matching (EBGM), which demonstrated excellent performance in the FERET evaluation test, has been considered as one of the best algorithms for face recognition due to its robustness against expression, illumination and pose variations. However, EBGM involves considerable computational complexity in its rigid and deformable matching process, preventing its use in many real-time applications. This paper presents a new Constrained Profile Model (CPM), in cooperation with Flexible Shape Model (FSM) to form an efficient localization framework. Through Gabor feature constrained local alignment, the proposed method not only avoids local minima in landmark localization, but also circumvents the exhaustive global optimization. Experiments on CAS-PEAL and FERET databases demonstrated the effectiveness and efficiency of the proposed method.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1016/j.patrec.2009.03.007
Copyright Statement Copyright 2009 Elsevier B.V. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Volume 30
Issue Number 10
Page from 922
Page to 930
ISSN 0167-8655
Date Accessioned 2009-07-06
Date Available 2010-06-18T03:53:10Z
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
URI http://hdl.handle.net/10072/28503
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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