{"title":"生物识别系统的信念网络设计","authors":"S. Yanushkevich","doi":"10.1109/CIBIM.2011.5949227","DOIUrl":null,"url":null,"abstract":"Bayesian belief networks represent a widely acceptable in biometric system design formalism for supporting reasoning when information is incomplete. Belief networks provide a coherent environment in which beliefs about target propositions can be re-evaluated using newly acquired evidence. This constitutes a fundamental prerequisite for decision making under uncertainty. This tutorial provides examples of applying the Bayesian decision profile in the multi-biometric system design, as well as in modeling attacks on biometric systems.","PeriodicalId":396721,"journal":{"name":"2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Belief network design for biometric systems\",\"authors\":\"S. Yanushkevich\",\"doi\":\"10.1109/CIBIM.2011.5949227\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Bayesian belief networks represent a widely acceptable in biometric system design formalism for supporting reasoning when information is incomplete. Belief networks provide a coherent environment in which beliefs about target propositions can be re-evaluated using newly acquired evidence. This constitutes a fundamental prerequisite for decision making under uncertainty. This tutorial provides examples of applying the Bayesian decision profile in the multi-biometric system design, as well as in modeling attacks on biometric systems.\",\"PeriodicalId\":396721,\"journal\":{\"name\":\"2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIBIM.2011.5949227\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIBIM.2011.5949227","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bayesian belief networks represent a widely acceptable in biometric system design formalism for supporting reasoning when information is incomplete. Belief networks provide a coherent environment in which beliefs about target propositions can be re-evaluated using newly acquired evidence. This constitutes a fundamental prerequisite for decision making under uncertainty. This tutorial provides examples of applying the Bayesian decision profile in the multi-biometric system design, as well as in modeling attacks on biometric systems.