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 | |
| 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
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Cite this
Zheng, X., Kamata, S. I., & Yu, L. (2010). Face recognition with local gradient derivative patterns. In TENCON 2010 - 2010 IEEE Region 10 Conference (pp. 667-670). [5686637] (IEEE Region 10 Annual International Conference, Proceedings/TENCON). https://doi.org/10.1109/TENCON.2010.5686637