Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor

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Title Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor
Author Zhang, Baochang; Gao, Yongsheng; Zhao, Sanqiang; Liu, Jianzhuang
Journal Name IEEE Transactions on Image Processing
Editor Editor-in-Chief: Thrasos Pappas
Year Published 2010
Place of publication United States
Publisher IEEE Signal Processing Society
Abstract This paper proposes a novel high-order local pattern descriptor, Local Derivative Pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The nth-order LDP is proposed to encode the (n-1)th-order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in Local Binary Pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1109/TIP.2009.2035882
Copyright Statement Copyright 2010 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 IEEE.
Volume 19
Issue Number 2
Page from 533
Page to 544
ISSN 1057-7149
Date Accessioned 2010-02-15
Date Available 2010-07-15T09:43:06Z
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/10072/32176
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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