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
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U2 - 10.1007/11569947_18
DO - 10.1007/11569947_18
M3 - Conference contribution
AN - SCOPUS:33646752559
SN - 3540294317
SN - 9783540294313
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
T2 - International Wokshop on Biometric Recognition Systems, IWBRS 2005: Advances in Biometric Person Authentication
Y2 - 22 October 2005 through 23 October 2005
ER -