{"title":"A SURVEY OF VISIBLE IRIS RECOGNITION","authors":"Yali Song, Yong-Xin He, Jin Zhang","doi":"10.5121/CSIT.2019.90302","DOIUrl":null,"url":null,"abstract":"In recent years, research on iris recognition in near-infrared has made great progress and achievements. However in many devices, such as most of the mobile phones, there is no near-infrared device embedded. In order to use iris recognition in these devices, iris recognition in visible light is needed, but there are many problems to use visible iris recognition, including low recognition rate, poor robustness and so on. In this paper, we first clarified the challenges in visible iris recognition. We evaluate the effectiveness of three traditional iris recognition on iris collected from smart phones in visible light. The results show that traditional methods achieve accuracy not exceeding 60% at best. Then we summarize the recent advances in visible iris recognition in three aspects: iris image acquisition, iris preprocessing and iris feature extraction methods. In the end, we list future research directions in visible iris recognition.","PeriodicalId":372948,"journal":{"name":"Computer Science & Information Technology (CS & IT )","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science & Information Technology (CS & IT )","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/CSIT.2019.90302","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In recent years, research on iris recognition in near-infrared has made great progress and achievements. However in many devices, such as most of the mobile phones, there is no near-infrared device embedded. In order to use iris recognition in these devices, iris recognition in visible light is needed, but there are many problems to use visible iris recognition, including low recognition rate, poor robustness and so on. In this paper, we first clarified the challenges in visible iris recognition. We evaluate the effectiveness of three traditional iris recognition on iris collected from smart phones in visible light. The results show that traditional methods achieve accuracy not exceeding 60% at best. Then we summarize the recent advances in visible iris recognition in three aspects: iris image acquisition, iris preprocessing and iris feature extraction methods. In the end, we list future research directions in visible iris recognition.