대한전자공학회:학술대회논문집 (Proceedings of the IEEK Conference)
- 대한전자공학회 2002년도 ITC-CSCC -3
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- Pages.1674-1677
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- 2002
Multiresolution Independent Component Analysis for Iris Identification
- Noh, Seung-In (Department of Electrical and Electronic Engineering, Yonsei University) ;
- Kwanghuk Pae (Department of Electrical and Electronic Engineering, Yonsei University) ;
- Lee, Chulhan (Department of Electrical and Electronic Engineering, Yonsei University) ;
- Kim, Jaihie (Department of Electrical and Electronic Engineering, Yonsei University)
- 발행 : 2002.07.01
초록
In this paper, the new method to extract the features of iris signals is proposed; Multiresolution ICA (M-ICA) provides good properties to represent signals with time-frequency. The conventional methods were to use the technique of filter bank analysis, while ICA is unsupervised learning algorithm using high-order statistics. M-ICA could make use of strengths of learn- ing method and multiresolution. Also, we performed comparative studies of different feature extraction techniques applied to personal identification using iris pat- tern. To measure goodness of methods, we use Fisher’s discriminant ratio to quantify the class-separability of features generated by various techniques.
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