{"title":"Iris verification system based on curvelet transform","authors":"Hanêne Guesmi, H. Trichili, A. Alimi, B. Solaiman","doi":"10.1109/ICCI-CC.2012.6311152","DOIUrl":null,"url":null,"abstract":"The performance of the iris verification process highly depends on its extractor of iris features. So, to reduce the dimensionality of the iris image and improve the recognition rate, an iris features extraction method based on curvelet transform is proposed and presented in this paper. Thus, our paper focuses on presenting of our curvelet-based iris features extraction method. This method consists of two steps: decompose images into set of sub-bands by the curvelet transform and automatic extraction of the most discriminative features of these sub-bands. An extensive experimental results show that the proposed method is effective and encouraging.","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCI-CC.2012.6311152","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
The performance of the iris verification process highly depends on its extractor of iris features. So, to reduce the dimensionality of the iris image and improve the recognition rate, an iris features extraction method based on curvelet transform is proposed and presented in this paper. Thus, our paper focuses on presenting of our curvelet-based iris features extraction method. This method consists of two steps: decompose images into set of sub-bands by the curvelet transform and automatic extraction of the most discriminative features of these sub-bands. An extensive experimental results show that the proposed method is effective and encouraging.