Embedding Non-Distortive Cancelable Face Template Generation

[2402.02540] Embedding Non-Distortive Cancelable Face Template Generation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2402.02540 (cs) [Submitted on 4 Feb 2024] Title: Embedding Non-Distortive Cancelable Face Template Generation Authors: Dmytro Zakharov , Oleksandr Kuznetsov , Emanuele Frontoni , Natalia Kryvinska View a PDF of the paper titled Embedding Non-Distortive Cancelable Face Template Generation, by Dmytro Zakharov and 3 other authors View PDF HTML (experimental) Abstract: Biometric authentication systems are crucial for security, but developing them involves various complexities, including privacy, security, and achieving high accuracy without directly storing pure biometric data in storage. We introduce an innovative image distortion technique that makes facial images unrecognizable to the eye but still identifiable by any custom embedding neural network model. Using the proposed approach, we test the reliability of biometric recognition networks by determining the maximum image distortion that does not change the predicted identity. Through experiments on MNIST and LFW datasets, we assess its effectiveness and compare it based on the traditional comparison metrics. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Cryptography and Security (cs.CR) Cite as: arXiv:2402.02540 [cs.CV] (or arXiv:2402.02540v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2402.02540 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Artificial Intelligence and Soft Computing, 212--223, 2025 Related DOI : https://doi.org/10.1007/978-3-031-84356-3_18 Focus to learn more DOI(s) linking to related resources Submission history From: Dmytro Zakharov [ view email ] [v1] Sun, 4 Feb 2024 15:39:18 UTC (1,228 KB) Full-text links: Access Paper: View a PDF of the paper titled Embedding Non-Distortive Cancelable Face Template Generation, by Dmytro Zakharov and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-02 Change to browse by: cs cs.CR References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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