Abstract
In this paper, we propose a new feature extraction method based on independent component analysis (ICA) for iris recognition, which is known as the most reliable biometric system. We extract iris features using a bank of filters which are selected from the ICA basis functions. The ICA basis functions themselves are sufficient to be used as filter kernels for extracting iris features because they are estimated by training iris signals. Using techniques of the ICA estimation, we generate many kinds of candidates ICA filters. To select the ICA filters for extracting salient features efficiently, we introduce the requirements of the ICA filter. Each ICA filter has a different filter size and a good discrimination power to identify iris pattern. Also, the correlation between bandwidths of the ICA filters is minimized. Experimental results show that the EER of proposed ICA filter bank is better than those of existing methods in both the Yonsei iris database and CASIA iris database.
| Original language | English |
|---|---|
| Title of host publication | Advances in Biometric Person Authentication - International Wokshop on Biometric Recognition Systems, IWBRS 2005, Proceedings |
| Pages | 142-149 |
| Number of pages | 8 |
| Volume | 3781 LNCS |
| DOIs | |
| Publication status | Published - 2005 Dec 1 |
| Event | International Wokshop on Biometric Recognition Systems, IWBRS 2005: Advances in Biometric Person Authentication - Beijing, China Duration: 2005 Oct 22 → 2005 Oct 23 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 3781 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | International Wokshop on Biometric Recognition Systems, IWBRS 2005: Advances in Biometric Person Authentication |
|---|---|
| Country | China |
| City | Beijing |
| Period | 05/10/22 → 05/10/23 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science
Cite this
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A new feature extraction method using the ICA filters for iris recognition system. / Noh, Seung In; Bae, Kwanghyuk; Park, Kang Ryoung; Kim, Jaihie.
Advances in Biometric Person Authentication - International Wokshop on Biometric Recognition Systems, IWBRS 2005, Proceedings. Vol. 3781 LNCS 2005. p. 142-149 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3781 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A new feature extraction method using the ICA filters for iris recognition system
AU - Noh, Seung In
AU - Bae, Kwanghyuk
AU - Park, Kang Ryoung
AU - Kim, Jaihie
PY - 2005/12/1
Y1 - 2005/12/1
N2 - In this paper, we propose a new feature extraction method based on independent component analysis (ICA) for iris recognition, which is known as the most reliable biometric system. We extract iris features using a bank of filters which are selected from the ICA basis functions. The ICA basis functions themselves are sufficient to be used as filter kernels for extracting iris features because they are estimated by training iris signals. Using techniques of the ICA estimation, we generate many kinds of candidates ICA filters. To select the ICA filters for extracting salient features efficiently, we introduce the requirements of the ICA filter. Each ICA filter has a different filter size and a good discrimination power to identify iris pattern. Also, the correlation between bandwidths of the ICA filters is minimized. Experimental results show that the EER of proposed ICA filter bank is better than those of existing methods in both the Yonsei iris database and CASIA iris database.
AB - In this paper, we propose a new feature extraction method based on independent component analysis (ICA) for iris recognition, which is known as the most reliable biometric system. We extract iris features using a bank of filters which are selected from the ICA basis functions. The ICA basis functions themselves are sufficient to be used as filter kernels for extracting iris features because they are estimated by training iris signals. Using techniques of the ICA estimation, we generate many kinds of candidates ICA filters. To select the ICA filters for extracting salient features efficiently, we introduce the requirements of the ICA filter. Each ICA filter has a different filter size and a good discrimination power to identify iris pattern. Also, the correlation between bandwidths of the ICA filters is minimized. Experimental results show that the EER of proposed ICA filter bank is better than those of existing methods in both the Yonsei iris database and CASIA iris database.
UR - http://www.scopus.com/inward/record.url?scp=33646752559&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646752559&partnerID=8YFLogxK
U2 - 10.1007/11569947_18
DO - 10.1007/11569947_18
M3 - Conference contribution
SN - 3540294317
SN - 9783540294313
VL - 3781 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 142
EP - 149
BT - Advances in Biometric Person Authentication - International Wokshop on Biometric Recognition Systems, IWBRS 2005, Proceedings
ER -