{"title":"一致的生物特征评分水平融合公式","authors":"J. Hube","doi":"10.1109/BTAS.2017.8272714","DOIUrl":null,"url":null,"abstract":"In an operational setting of key practical importance for a biometric application deployment is the ability to set thresholds to meet error rate targets. Consequently there is a need to consider how output scores from multi-modal score-level fusion are defined. We show a method to ensure these fused scores are consistent with a known input score definition. We derive fusion formulae for the case of input scores based on false acceptance rates. We provide examples to highlight implementation issues.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Formulae for consistent biometric score level fusion\",\"authors\":\"J. Hube\",\"doi\":\"10.1109/BTAS.2017.8272714\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In an operational setting of key practical importance for a biometric application deployment is the ability to set thresholds to meet error rate targets. Consequently there is a need to consider how output scores from multi-modal score-level fusion are defined. We show a method to ensure these fused scores are consistent with a known input score definition. We derive fusion formulae for the case of input scores based on false acceptance rates. We provide examples to highlight implementation issues.\",\"PeriodicalId\":372008,\"journal\":{\"name\":\"2017 IEEE International Joint Conference on Biometrics (IJCB)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Joint Conference on Biometrics (IJCB)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BTAS.2017.8272714\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Joint Conference on Biometrics (IJCB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BTAS.2017.8272714","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Formulae for consistent biometric score level fusion
In an operational setting of key practical importance for a biometric application deployment is the ability to set thresholds to meet error rate targets. Consequently there is a need to consider how output scores from multi-modal score-level fusion are defined. We show a method to ensure these fused scores are consistent with a known input score definition. We derive fusion formulae for the case of input scores based on false acceptance rates. We provide examples to highlight implementation issues.