{"title":"Strategy for improving the reliability in the facial identification","authors":"C. Travieso, J. B. Alonso, Miguel A. Ferrer","doi":"10.1109/CCST.2005.1594833","DOIUrl":null,"url":null,"abstract":"This paper presents a simple, robust and novel for errors detection in biometric system which is applied to the Olivetti Research Laboratory (ORL) face database (400 images). We have used as parameterisation different transformed dominions (Travieso et al., 2004; Faundez, 2003), and a support vector machine (SVM) (Burges, 1998; Cristianini and Shawe-Taylor, 2000) as classifier. This system has been adjusted with our experiments for obtaining a false identification rate (FIR) of 0%, with a success rate of 90.8% a rejected samples rate of 9.2%.","PeriodicalId":411051,"journal":{"name":"Proceedings 39th Annual 2005 International Carnahan Conference on Security Technology","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 39th Annual 2005 International Carnahan Conference on Security Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCST.2005.1594833","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
This paper presents a simple, robust and novel for errors detection in biometric system which is applied to the Olivetti Research Laboratory (ORL) face database (400 images). We have used as parameterisation different transformed dominions (Travieso et al., 2004; Faundez, 2003), and a support vector machine (SVM) (Burges, 1998; Cristianini and Shawe-Taylor, 2000) as classifier. This system has been adjusted with our experiments for obtaining a false identification rate (FIR) of 0%, with a success rate of 90.8% a rejected samples rate of 9.2%.
本文提出了一种简单、鲁棒、新颖的生物识别错误检测系统，并将其应用于Olivetti研究实验室(ORL)人脸数据库(400张图像)。我们使用不同的转换域作为参数化(Travieso et al.， 2004;Faundez, 2003)和支持向量机(SVM) (Burges, 1998;Cristianini and Shawe-Taylor, 2000)作为分类器。通过实验对该系统进行了调整，获得了误识别率(FIR)为0%，成功率为90.8%，拒收率为9.2%。
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