P. Lai, J. O’Sullivan, M. Chen, E. Sirevaag, A. D. Kaplan, J. Rohrbaugh
{"title":"A robust feature selection method for noncontact biometrics based on Laser Doppler Vibrometry","authors":"P. Lai, J. O’Sullivan, M. Chen, E. Sirevaag, A. D. Kaplan, J. Rohrbaugh","doi":"10.1109/BSYM.2008.4655524","DOIUrl":null,"url":null,"abstract":"We propose a new biometric approach based on cardiovascular signals recorded using laser Doppler vibrometry (LDV) with a robust feature selection method. A novel feature selection method provides robustness against physiological variability of a given individual. LDV signals were collected from 191 individuals under controlled conditions during three sessions, each at intervals of one week to six months. The methods described here are based on a time-frequency decomposition of the LDV signal in which the log-power of the decomposition values are used as features. In identity verification tasks, equal error rates in the single digits can be achieved with testing periods as short as 4 s.","PeriodicalId":389538,"journal":{"name":"2008 Biometrics Symposium","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Biometrics Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BSYM.2008.4655524","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
We propose a new biometric approach based on cardiovascular signals recorded using laser Doppler vibrometry (LDV) with a robust feature selection method. A novel feature selection method provides robustness against physiological variability of a given individual. LDV signals were collected from 191 individuals under controlled conditions during three sessions, each at intervals of one week to six months. The methods described here are based on a time-frequency decomposition of the LDV signal in which the log-power of the decomposition values are used as features. In identity verification tasks, equal error rates in the single digits can be achieved with testing periods as short as 4 s.