{"title":"基于曲线变换和粒子群优化的虹膜识别","authors":"A. Ahamed, Syed Irfan Ali Meerza","doi":"10.1109/WNYISPW57858.2022.9983494","DOIUrl":null,"url":null,"abstract":"This study proposes a low complexity iris recognition technique using particle swarm optimization in the curvelet domain transformation. We utilize the standard CASIA-Iris V4 database to test the performance of our proposed method as compared to other state-of-the-art methods. The proposed method provides 99.4% accuracy in recognizing iris images. In addition, our proposed method requires 50% less computational time compared to other state-of-the-art methods.","PeriodicalId":427869,"journal":{"name":"2022 IEEE Western New York Image and Signal Processing Workshop (WNYISPW)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Iris recognition using curvelet transform and accuracy maximization by particle swarm optimization\",\"authors\":\"A. Ahamed, Syed Irfan Ali Meerza\",\"doi\":\"10.1109/WNYISPW57858.2022.9983494\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study proposes a low complexity iris recognition technique using particle swarm optimization in the curvelet domain transformation. We utilize the standard CASIA-Iris V4 database to test the performance of our proposed method as compared to other state-of-the-art methods. The proposed method provides 99.4% accuracy in recognizing iris images. In addition, our proposed method requires 50% less computational time compared to other state-of-the-art methods.\",\"PeriodicalId\":427869,\"journal\":{\"name\":\"2022 IEEE Western New York Image and Signal Processing Workshop (WNYISPW)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Western New York Image and Signal Processing Workshop (WNYISPW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WNYISPW57858.2022.9983494\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Western New York Image and Signal Processing Workshop (WNYISPW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WNYISPW57858.2022.9983494","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Iris recognition using curvelet transform and accuracy maximization by particle swarm optimization
This study proposes a low complexity iris recognition technique using particle swarm optimization in the curvelet domain transformation. We utilize the standard CASIA-Iris V4 database to test the performance of our proposed method as compared to other state-of-the-art methods. The proposed method provides 99.4% accuracy in recognizing iris images. In addition, our proposed method requires 50% less computational time compared to other state-of-the-art methods.