Face recognition with local gradient derivative patterns

Xianchun Zheng; Sei Ichiro Kamata; Liang Yu

doi:10.1109/TENCON.2010.5686637

Face recognition with local gradient derivative patterns

Xianchun Zheng, Sei Ichiro Kamata, Liang Yu

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In this work, we present a novel local pattern descriptor, Local Gradient Derivative Pattern (LGDP) to face recognition which considers more detailed information than the Local Binary Pattern (LBP). The face image is first divided into several small regions from which Local Gradient Derivative Pattern (LGDP) histograms are extracted and concatenated into a single, spatially enhanced feature vector to be used as a face descriptor. Three well-known and challenge-ORL, Yale and FERET face databases are used in the performances to evaluate the method. The experiments result clearly show that the proposed method give us a better performance than some other methods.

Original language	English
Title of host publication	TENCON 2010 - 2010 IEEE Region 10 Conference
Pages	667-670
Number of pages	4
DOIs	https://doi.org/10.1109/TENCON.2010.5686637
Publication status	Published - 2010 Dec 1
Event	2010 IEEE Region 10 Conference, TENCON 2010 - Fukuoka, Japan Duration: 2010 Nov 21 → 2010 Nov 24

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON

Other

Other	2010 IEEE Region 10 Conference, TENCON 2010
Country	Japan
City	Fukuoka
Period	10/11/21 → 10/11/24

Keywords

Face recognition
Histogram
Local Gradient Derivative Patterns (LGDP)

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TENCON.2010.5686637

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Cite this

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title = "Face recognition with local gradient derivative patterns",

abstract = "In this work, we present a novel local pattern descriptor, Local Gradient Derivative Pattern (LGDP) to face recognition which considers more detailed information than the Local Binary Pattern (LBP). The face image is first divided into several small regions from which Local Gradient Derivative Pattern (LGDP) histograms are extracted and concatenated into a single, spatially enhanced feature vector to be used as a face descriptor. Three well-known and challenge-ORL, Yale and FERET face databases are used in the performances to evaluate the method. The experiments result clearly show that the proposed method give us a better performance than some other methods.",

keywords = "Face recognition, Histogram, Local Gradient Derivative Patterns (LGDP)",

author = "Xianchun Zheng and Kamata, {Sei Ichiro} and Liang Yu",

year = "2010",

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doi = "10.1109/TENCON.2010.5686637",

language = "English",

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AU - Zheng, Xianchun

AU - Kamata, Sei Ichiro

AU - Yu, Liang

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N2 - In this work, we present a novel local pattern descriptor, Local Gradient Derivative Pattern (LGDP) to face recognition which considers more detailed information than the Local Binary Pattern (LBP). The face image is first divided into several small regions from which Local Gradient Derivative Pattern (LGDP) histograms are extracted and concatenated into a single, spatially enhanced feature vector to be used as a face descriptor. Three well-known and challenge-ORL, Yale and FERET face databases are used in the performances to evaluate the method. The experiments result clearly show that the proposed method give us a better performance than some other methods.

AB - In this work, we present a novel local pattern descriptor, Local Gradient Derivative Pattern (LGDP) to face recognition which considers more detailed information than the Local Binary Pattern (LBP). The face image is first divided into several small regions from which Local Gradient Derivative Pattern (LGDP) histograms are extracted and concatenated into a single, spatially enhanced feature vector to be used as a face descriptor. Three well-known and challenge-ORL, Yale and FERET face databases are used in the performances to evaluate the method. The experiments result clearly show that the proposed method give us a better performance than some other methods.

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KW - Histogram

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