{"title":"Multi-sample Compression of Finger Vein Images using H.265 Video Coding","authors":"Kevin Schörgnhofer, Sami Dafir, A. Uhl","doi":"10.1109/ICB45273.2019.8987412","DOIUrl":null,"url":null,"abstract":"A new video-compression based approach extending traditional biometric sample data compression techniques is evaluated in the context of finger vein recognition. The proposed scheme is implemented in HEVC / H.265 in different settings and compared to (i) compressing each sample individually with JPEG2000 according to ISO/IEC 19794-9:2011 and to (ii) compressing each users’ data into an individual video file. Compression efficiency and implications on recognition accuracy are determined using 4 recognition schemes and 2 data sets, both based on publicly available data. Results obtained using the proposed approach are fairly stable across different recognition schemes and data sets and indicate a significant improvement over the current state of the art.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Biometrics (ICB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICB45273.2019.8987412","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A new video-compression based approach extending traditional biometric sample data compression techniques is evaluated in the context of finger vein recognition. The proposed scheme is implemented in HEVC / H.265 in different settings and compared to (i) compressing each sample individually with JPEG2000 according to ISO/IEC 19794-9:2011 and to (ii) compressing each users’ data into an individual video file. Compression efficiency and implications on recognition accuracy are determined using 4 recognition schemes and 2 data sets, both based on publicly available data. Results obtained using the proposed approach are fairly stable across different recognition schemes and data sets and indicate a significant improvement over the current state of the art.