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 | IEEE Region 10 Annual International Conference, Proceedings/TENCON |
| Pages | 667-670 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 2010 IEEE Region 10 Conference, TENCON 2010 - Fukuoka Duration: 2010 Nov 21 → 2010 Nov 24 |
Other
| Other | 2010 IEEE Region 10 Conference, TENCON 2010 |
|---|---|
| City | Fukuoka |
| Period | 10/11/21 → 10/11/24 |
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Keywords
- Face recognition
- Histogram
- Local Gradient Derivative Patterns (LGDP)
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
Cite this
Face recognition with local gradient derivative patterns. / Zheng, Xianchun; Kamata, Seiichiro; Yu, Liang.
IEEE Region 10 Annual International Conference, Proceedings/TENCON. 2010. p. 667-670 5686637.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Face recognition with local gradient derivative patterns
AU - Zheng, Xianchun
AU - Kamata, Seiichiro
AU - Yu, Liang
PY - 2010
Y1 - 2010
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.
KW - Face recognition
KW - Histogram
KW - Local Gradient Derivative Patterns (LGDP)
UR - http://www.scopus.com/inward/record.url?scp=79951650797&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79951650797&partnerID=8YFLogxK
U2 - 10.1109/TENCON.2010.5686637
DO - 10.1109/TENCON.2010.5686637
M3 - Conference contribution
AN - SCOPUS:79951650797
SN - 9781424468904
SP - 667
EP - 670
BT - IEEE Region 10 Annual International Conference, Proceedings/TENCON
ER -