{"title":"基于H.265视频编码的手指静脉图像多样本压缩","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":"{\"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}","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}
Multi-sample Compression of Finger Vein Images using H.265 Video Coding
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.