Abstract
Illumination change is one of most important and difficult problems which prevent from applying face recognition to real applications. For solving this, we propose a method to compensate for different illumination conditions based on SVDD(Support Vector Data Description). In the proposed method, we first consider the SVDD training for the data belonging to the facial images under various illuminations, and model the data region for each illumination as the ball resulting from the SVDD training. Next, we compensate for illumination changes using feature vector projection onto the decision boundary of the SVDD ball. Finally, we obtain the pre-image under the identical illumination with input image. By repeated for each person, we can recognize a person with facial images under same illumination. We also perform the face recognition in order to verify the efficacy of proposed method.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 154-162 |
| Number of pages | 9 |
| Volume | 4642 LNCS |
| Publication status | Published - 2007 Dec 1 |
| Event | 2007 International Conference on Advances in Biometrics, ICB 2007 - Seoul, Korea, Republic of Duration: 2007 Aug 27 → 2007 Aug 29 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 4642 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 2007 International Conference on Advances in Biometrics, ICB 2007 |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 07/8/27 → 07/8/29 |
Fingerprint
Keywords
- Face recognition
- Face reconstruction
- Illumination compensation
- Noise
- Support vector data description
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Computer Science(all)
- Theoretical Computer Science
Cite this
SVDD-Based illumination compensation for face recognition. / Lee, Sang Woong; Lee, Seong Whan.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4642 LNCS 2007. p. 154-162 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4642 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - SVDD-Based illumination compensation for face recognition
AU - Lee, Sang Woong
AU - Lee, Seong Whan
PY - 2007/12/1
Y1 - 2007/12/1
N2 - Illumination change is one of most important and difficult problems which prevent from applying face recognition to real applications. For solving this, we propose a method to compensate for different illumination conditions based on SVDD(Support Vector Data Description). In the proposed method, we first consider the SVDD training for the data belonging to the facial images under various illuminations, and model the data region for each illumination as the ball resulting from the SVDD training. Next, we compensate for illumination changes using feature vector projection onto the decision boundary of the SVDD ball. Finally, we obtain the pre-image under the identical illumination with input image. By repeated for each person, we can recognize a person with facial images under same illumination. We also perform the face recognition in order to verify the efficacy of proposed method.
AB - Illumination change is one of most important and difficult problems which prevent from applying face recognition to real applications. For solving this, we propose a method to compensate for different illumination conditions based on SVDD(Support Vector Data Description). In the proposed method, we first consider the SVDD training for the data belonging to the facial images under various illuminations, and model the data region for each illumination as the ball resulting from the SVDD training. Next, we compensate for illumination changes using feature vector projection onto the decision boundary of the SVDD ball. Finally, we obtain the pre-image under the identical illumination with input image. By repeated for each person, we can recognize a person with facial images under same illumination. We also perform the face recognition in order to verify the efficacy of proposed method.
KW - Face recognition
KW - Face reconstruction
KW - Illumination compensation
KW - Noise
KW - Support vector data description
UR - http://www.scopus.com/inward/record.url?scp=38049067114&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38049067114&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:38049067114
SN - 9783540745488
VL - 4642 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 154
EP - 162
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -