Javier Galbally, F. Alonso-Fernandez, Julian Fierrez, J. Ortega-Garcia
{"title":"Fingerprint liveness detection based on quality measures","authors":"Javier Galbally, F. Alonso-Fernandez, Julian Fierrez, J. Ortega-Garcia","doi":"10.1109/BIDS.2009.5507534","DOIUrl":null,"url":null,"abstract":"A new fingerprint parameterization for liveness detection based on quality measures is presented. The novel feature set is used in a complete liveness detection system and tested on the development set of the LivDET competition, comprising over 4,500 real and fake images acquired with three different optical sensors. The proposed solution proves to be robust to the multi-sensor scenario, and presents an overall rate of 93% of correctly classified samples. Furthermore, the liveness detection method presented has the added advantage over previously studied techniques of needing just one image from a finger to decide whether it is real or fake.","PeriodicalId":409188,"journal":{"name":"2009 First IEEE International Conference on Biometrics, Identity and Security (BIdS)","volume":"11 25","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"35","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 First IEEE International Conference on Biometrics, Identity and Security (BIdS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIDS.2009.5507534","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 35
Abstract
A new fingerprint parameterization for liveness detection based on quality measures is presented. The novel feature set is used in a complete liveness detection system and tested on the development set of the LivDET competition, comprising over 4,500 real and fake images acquired with three different optical sensors. The proposed solution proves to be robust to the multi-sensor scenario, and presents an overall rate of 93% of correctly classified samples. Furthermore, the liveness detection method presented has the added advantage over previously studied techniques of needing just one image from a finger to decide whether it is real or fake.
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