Iris Recognition Based on a Shift-Invariant Wavelet TransformIris Recognition Based on a Shift-Invariant Wavelet Transform
- Other Titles
- Iris Recognition Based on a Shift-Invariant Wavelet Transform
- Authors
- 조성원; Haemin Kim
- Issue Date
- 2004
- Publisher
- 한국지능시스템학회
- Keywords
- iris recognition; shift-invariant wavelet transform; similarity measure
- Citation
- International Journal of Fuzzy Logic and Intelligent systems, v.4, no.3, pp.322 - 326
- Journal Title
- International Journal of Fuzzy Logic and Intelligent systems
- Volume
- 4
- Number
- 3
- Start Page
- 322
- End Page
- 326
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/26188
- ISSN
- 1598-2645
- Abstract
- This paper describes a new iris recognition method based on a shift-invariant wavelet sub-images. For the feature representation, we first preprocess an iris image for the compensation of the variation of the iris and for the easy implementation of the wavelet transform. Then, we decompose the preprocessed iris image into multiple subband images using a shift-invariant wavelet transform. For feature representation, we select a set of subband images, which have rich information for the classification of various iris patterns and robust to noises. In order to reduce the size of the feature vector, we quantize.each pixel of subband images using the Lloyd-Max quantization method Each feature element is represented by one of quantization levels, and a set of these feature element is the feature vector. When the quantization is very coarse, the quantized level does not have much information about the image pixel value. Therefore, we define a new similarity measure based on mutual information between two features. With this similarity measure, the size of the feature vector can be reduced without much degradation of performance. Experimentally, we show that the proposed method produced superb performance in iris recognition.
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Collections - College of Engineering > School of Electronic & Electrical Engineering > 1. Journal Articles
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