{"title":"基于局部图像描述符和PRNU融合的虹膜图像起源识别","authors":"Christof Kauba, L. Debiasi, A. Uhl","doi":"10.1109/BTAS.2017.8272710","DOIUrl":null,"url":null,"abstract":"Being aware of the origin (source sensor) of an iris images offers several advantages. Identifying the specific sensor unit supports ensuring the integrity and authenticity of iris images and thus detecting insertion attacks at a biometric system. Moreover, by knowing the sensor model selective processing, such as image enhancements, becomes feasible. In order to determine the origin (i.e. dataset) of near-infrared (NIR) and visible spectrum iris/ocular images, we evaluate the performance of three different approaches, a photo response non-uniformity (PRNU) based and an image texture feature based one, and the fusion of both. Our first set of experiments includes 19 different datasets comprising different sensors and image resolutions. The second set includes 6 different camera models with 5 instances each. We evaluate the applicability of the three approaches in these test scenarios from a forensic and non-forensic perspective.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Identifying the origin of Iris images based on fusion of local image descriptors and PRNU based techniques\",\"authors\":\"Christof Kauba, L. Debiasi, A. Uhl\",\"doi\":\"10.1109/BTAS.2017.8272710\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Being aware of the origin (source sensor) of an iris images offers several advantages. Identifying the specific sensor unit supports ensuring the integrity and authenticity of iris images and thus detecting insertion attacks at a biometric system. Moreover, by knowing the sensor model selective processing, such as image enhancements, becomes feasible. In order to determine the origin (i.e. dataset) of near-infrared (NIR) and visible spectrum iris/ocular images, we evaluate the performance of three different approaches, a photo response non-uniformity (PRNU) based and an image texture feature based one, and the fusion of both. Our first set of experiments includes 19 different datasets comprising different sensors and image resolutions. The second set includes 6 different camera models with 5 instances each. We evaluate the applicability of the three approaches in these test scenarios from a forensic and non-forensic perspective.\",\"PeriodicalId\":372008,\"journal\":{\"name\":\"2017 IEEE International Joint Conference on Biometrics (IJCB)\",\"volume\":\"121 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Joint Conference on Biometrics (IJCB)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BTAS.2017.8272710\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Joint Conference on Biometrics (IJCB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BTAS.2017.8272710","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identifying the origin of Iris images based on fusion of local image descriptors and PRNU based techniques
Being aware of the origin (source sensor) of an iris images offers several advantages. Identifying the specific sensor unit supports ensuring the integrity and authenticity of iris images and thus detecting insertion attacks at a biometric system. Moreover, by knowing the sensor model selective processing, such as image enhancements, becomes feasible. In order to determine the origin (i.e. dataset) of near-infrared (NIR) and visible spectrum iris/ocular images, we evaluate the performance of three different approaches, a photo response non-uniformity (PRNU) based and an image texture feature based one, and the fusion of both. Our first set of experiments includes 19 different datasets comprising different sensors and image resolutions. The second set includes 6 different camera models with 5 instances each. We evaluate the applicability of the three approaches in these test scenarios from a forensic and non-forensic perspective.