{"title":"A Short Review of Multimodal Biometric Recognition Systems","authors":"N. Celik","doi":"10.4172/2155-6180.1000355","DOIUrl":null,"url":null,"abstract":"Multimodal biometric systems, which combine two unimodal recognition systems into one single method, can be used to overcome the limitations of individual biometrics. This paper will do a short but critical review on recently developed for enhancing multimodal biometric systems. As can be seen from Celik et al. [1] the biometric information can be combined using different types of fusion of biometric data at different levels, i.e., at the feature level, matchingscore level or decision level. The biometric data classification and throughput of the biometric recognition systems can be carried out by analysing these fusion levels.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000355","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of biometrics & biostatistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4172/2155-6180.1000355","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Multimodal biometric systems, which combine two unimodal recognition systems into one single method, can be used to overcome the limitations of individual biometrics. This paper will do a short but critical review on recently developed for enhancing multimodal biometric systems. As can be seen from Celik et al. [1] the biometric information can be combined using different types of fusion of biometric data at different levels, i.e., at the feature level, matchingscore level or decision level. The biometric data classification and throughput of the biometric recognition systems can be carried out by analysing these fusion levels.
多模态生物识别系统将两个单模态识别系统结合成一个单一的方法,可以用来克服个体生物识别的局限性。本文将对最近发展起来的增强多模态生物识别系统做一个简短但重要的回顾。从Celik et al.[1]可以看出,生物特征信息可以通过不同类型的生物特征数据融合在不同的层次上进行组合,即特征层、匹配得分层或决策层。通过分析这些融合水平,可以实现生物特征数据的分类和生物特征识别系统的吞吐量。