{"title":"Using hand knuckle texture for biometric identification","authors":"M. Ferrer, C. Travieso, J. B. Alonso","doi":"10.1109/ccst.2005.1594835","DOIUrl":null,"url":null,"abstract":"In this paper is proposed a first approach of a novel and simple biometric verification system based on the texture of the hand knuckles. A knuckle image with scale, rotation and translation invariance is isolated from the hand recorded image with digital image processing techniques. The wrinkle of the knuckle images are extracted to a black and white image which is used as biometric feature. A hidden Markov model and a support vector machine are proposed as verifiers. A similar equal error rate of 0.094 is reached by both classifiers with our preliminary database consisting of 8 samples of 20 people hand.","PeriodicalId":411051,"journal":{"name":"Proceedings 39th Annual 2005 International Carnahan Conference on Security Technology","volume":"134 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","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.2005.1594835","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In this paper is proposed a first approach of a novel and simple biometric verification system based on the texture of the hand knuckles. A knuckle image with scale, rotation and translation invariance is isolated from the hand recorded image with digital image processing techniques. The wrinkle of the knuckle images are extracted to a black and white image which is used as biometric feature. A hidden Markov model and a support vector machine are proposed as verifiers. A similar equal error rate of 0.094 is reached by both classifiers with our preliminary database consisting of 8 samples of 20 people hand.