Abstract
Human face is one of the most common and useful keys to a person's identity. Many algorithms have been developed for automatic face recognition. And a number of commercial products have reached the market already. In general, however, many believe that the technology has yet to improve further, particularly to overcome the instability due to variable illuminations, expressions, poses and accessories. These variations often lead to large nonlinear variation in facial image. To date it is a very important issue to understand the limitation of the current face recognition technology. In this paper, we report the experimental result of face recognition performed using PCA (Principal Component Analysis), LFA (Local Feature Analysis) and correlation matching algorithms on the KFDB (Korean Face Database) which contains Korean face images taken under various conditions.
| Original language | English |
|---|---|
| Pages (from-to) | 557-565 |
| Number of pages | 9 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 2688 |
| Publication status | Published - 2003 Dec 1 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science
Cite this
Performance Evaluation of Face Recognition Algorithms on the Asian Face Database, KFDB. / Hwang, Bon Woo; Byun, Hyeran; Roh, Myoung Cheol; Lee, Seong Whan.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2688, 01.12.2003, p. 557-565.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Performance Evaluation of Face Recognition Algorithms on the Asian Face Database, KFDB
AU - Hwang, Bon Woo
AU - Byun, Hyeran
AU - Roh, Myoung Cheol
AU - Lee, Seong Whan
PY - 2003/12/1
Y1 - 2003/12/1
N2 - Human face is one of the most common and useful keys to a person's identity. Many algorithms have been developed for automatic face recognition. And a number of commercial products have reached the market already. In general, however, many believe that the technology has yet to improve further, particularly to overcome the instability due to variable illuminations, expressions, poses and accessories. These variations often lead to large nonlinear variation in facial image. To date it is a very important issue to understand the limitation of the current face recognition technology. In this paper, we report the experimental result of face recognition performed using PCA (Principal Component Analysis), LFA (Local Feature Analysis) and correlation matching algorithms on the KFDB (Korean Face Database) which contains Korean face images taken under various conditions.
AB - Human face is one of the most common and useful keys to a person's identity. Many algorithms have been developed for automatic face recognition. And a number of commercial products have reached the market already. In general, however, many believe that the technology has yet to improve further, particularly to overcome the instability due to variable illuminations, expressions, poses and accessories. These variations often lead to large nonlinear variation in facial image. To date it is a very important issue to understand the limitation of the current face recognition technology. In this paper, we report the experimental result of face recognition performed using PCA (Principal Component Analysis), LFA (Local Feature Analysis) and correlation matching algorithms on the KFDB (Korean Face Database) which contains Korean face images taken under various conditions.
UR - http://www.scopus.com/inward/record.url?scp=34548742699&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34548742699&partnerID=8YFLogxK
M3 - Article
VL - 2688
SP - 557
EP - 565
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SN - 0302-9743
ER -