{"title":"一种改进的基于心音和高斯混合模型的生物识别系统","authors":"F. Beritelli, Andrea Spadaccini","doi":"10.1109/BIOMS.2010.5610442","DOIUrl":null,"url":null,"abstract":"This paper presents an evolution of a biometric identity verification system based on heart sounds. The system is built using Gaussian Mixture Models (GMMs) and uses features extracted both from the spectral domain and the time domain in order to improve the performance, measured in terms of Equal Error Rate (EER), with respect to similar systems. The best result obtained using our approach, computed over a database of 165 people, is an EER of 13,70 %, that outperforms other similar approaches.","PeriodicalId":179925,"journal":{"name":"2010 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"An improved biometric identification system based on heart sounds and Gaussian Mixture Models\",\"authors\":\"F. Beritelli, Andrea Spadaccini\",\"doi\":\"10.1109/BIOMS.2010.5610442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an evolution of a biometric identity verification system based on heart sounds. The system is built using Gaussian Mixture Models (GMMs) and uses features extracted both from the spectral domain and the time domain in order to improve the performance, measured in terms of Equal Error Rate (EER), with respect to similar systems. The best result obtained using our approach, computed over a database of 165 people, is an EER of 13,70 %, that outperforms other similar approaches.\",\"PeriodicalId\":179925,\"journal\":{\"name\":\"2010 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIOMS.2010.5610442\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIOMS.2010.5610442","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An improved biometric identification system based on heart sounds and Gaussian Mixture Models
This paper presents an evolution of a biometric identity verification system based on heart sounds. The system is built using Gaussian Mixture Models (GMMs) and uses features extracted both from the spectral domain and the time domain in order to improve the performance, measured in terms of Equal Error Rate (EER), with respect to similar systems. The best result obtained using our approach, computed over a database of 165 people, is an EER of 13,70 %, that outperforms other similar approaches.
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