Iris recognition using wavelet features
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, J | - |
dc.contributor.author | Cho, SW | - |
dc.contributor.author | Choi, J | - |
dc.contributor.author | Marks, RJ | - |
dc.date.accessioned | 2022-02-18T07:41:27Z | - |
dc.date.available | 2022-02-18T07:41:27Z | - |
dc.date.created | 2022-02-18 | - |
dc.date.issued | 2004-09 | - |
dc.identifier.issn | 0922-5773 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/25750 | - |
dc.description.abstract | The traditional iris recognition systems require equal high quality human iris images. A cheap image acquisition system has difficulty in capturing equal high quality iris images. This paper describes a new feature representation method for iris recognition robust to noises. The disc-shaped iris image is first convolved with a low pass filter along the radial direction. Then, the radially smoothed iris image is decomposed in the angular direction using a one-dimensional continuous wavelet transforrn. Each decomposed one-dimensional waveform is approximated by an optimal piecewise linear curve connecting a small set of node points. The set of node points is used as a feature vector. The optimal approximation procedure reduces the feature vector size while maintaining recognition accuracy. The similarity between two iris images is measured by the normalized cross-correlation coefficients between optimal curves. The similarity between two iris images is estimated using mid-frequency bands. The rotation of one-dimensional signals due to the head tilt is estimated using the lowest frequency component. Experimentally we show the proposed method produces superb performance in iris recognition. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.title | Iris recognition using wavelet features | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, J | - |
dc.contributor.affiliatedAuthor | Cho, SW | - |
dc.identifier.doi | 10.1023/B:VLSI.0000040426.72253.b1 | - |
dc.identifier.scopusid | 2-s2.0-4444276347 | - |
dc.identifier.wosid | 000224675700005 | - |
dc.identifier.bibliographicCitation | JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, v.38, no.2, pp.147 - 156 | - |
dc.relation.isPartOf | JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY | - |
dc.citation.title | JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY | - |
dc.citation.volume | 38 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 147 | - |
dc.citation.endPage | 156 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordAuthor | iris recognition | - |
dc.subject.keywordAuthor | continuous wavelet transform | - |
dc.subject.keywordAuthor | feature representation | - |
dc.subject.keywordAuthor | curve optimization | - |
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