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

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Zhang, Baochang
Gao, Yongsheng
Zhao, Sanqiang
Liu, Jianzhuang
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Editor-in-Chief: Thrasos Pappas

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2010
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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.

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IEEE Transactions on Image Processing

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19

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2

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© 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.

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Cognitive and computational psychology

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