{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fdfnet-a-secure-cancelable-deep-finger-dorsal","title":"FDFNet : A Secure Cancelable Deep Finger Dorsal Template Generation Network Secured via. Bio-Hashing","arxiv_id":"1812.05308","date":"2018-12-13","proceeding":null,"authors":["Avantika Singh","Ashish Arora","Shreya Hasmukh Patel","Gaurav Jaswal","Aditya Nigam"],"abstract":"Present world has already been consistently exploring the fine edges of\nonline and digital world by imposing multiple challenging problems/scenarios.\nSimilar to physical world, personal identity management is very crucial\nin-order to provide any secure online system. Last decade has seen a lot of\nwork in this area using biometrics such as face, fingerprint, iris etc. Still\nthere exist several vulnerabilities and one should have to address the problem\nof compromised biometrics much more seriously, since they cannot be modified\neasily once compromised. In this work, we have proposed a secure cancelable\nfinger dorsal template generation network (learning domain specific features)\nsecured via. Bio-Hashing. Proposed system effectively protects the original\nfinger dorsal images by withdrawing compromised template and reassigning the\nnew one. A novel Finger-Dorsal Feature Extraction Net (FDFNet) has been\nproposed for extracting the discriminative features. This network is\nexclusively trained on trait specific features without using any kind of\npre-trained architecture. Later Bio-Hashing, a technique based on assigning a\ntokenized random number to each user, has been used to hash the features\nextracted from FDFNet. To test the performance of the proposed architecture, we\nhave tested it over two benchmark public finger knuckle datasets: PolyU FKP and\nPolyU Contactless FKI. The experimental results shows the effectiveness of the\nproposed system in terms of security and accuracy.","url_abs":"http://arxiv.org/abs/1812.05308v1","url_pdf":"http://arxiv.org/pdf/1812.05308v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fdfnet-a-secure-cancelable-deep-finger-dorsal","repo_url":"https://github.com/ashisharora010/FDFNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"fdfnet-a-secure-cancelable-deep-finger-dorsal","repo_url":"https://github.com/ashisharora010/data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}