Optimizing Secure Selective Face Template Generation Exchange over Open Network

Optimizing Secure Selective Face Template Generation Exchange over Open Network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimizing Secure Selective Face Template Generation Exchange over Open Network A. M. Ayoup, Soha Safwat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6550972/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Many modern authentication systems utilize human biometrics instead of traditional passwords and security codes to overcome their limitations. To enhance security, cancellable biometric transformations are employed to make it more difficult to retrieve the original biometric data. This paper introduces a new method for cancellable face recognition using Elliptic Curve Cryptography (ECC) along with selective biometric techniques. The goal of this approach is to strengthen the security of biometric traits, protect against potential attacks, and facilitate the safe transfer of biometric templates over open networks and within low-storage databases. By generating cancellable biometric patterns from the originals, this system can effectively manage access while addressing common issues associated with biometric systems, such as sensor data corruption, missing characters, under-representation, overshoot, and incompleteness. Additionally, a multi-modal biometric identification system can lower Failure-To-Capture (FTC) and Failure-To-Enroll (FTE) rates, providing strong protection against counterfeiting. To improve the implementation of multi-modal biometric traits, the time-consuming ECC multiplication operation is circumvented by using selective or partial biometrics as the template for each user, making it more suitable for real-time applications. Importantly, the proposed framework ensures complete distortion and encryption of unique biometric traits to protect them from unauthorized access. The efficiency and robustness of this approach have been validated using two sets of face biometric databases. Performance was evaluated using Receiver Operating Characteristic (ROC) curves and correlation scores, with simulation results demonstrating the effectiveness and potential of the proposed method. Viola-Jones Algorithm (Machine Learning -ML) Elliptic Curve Cryptography ROC Authentication System Decimation Technique Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6550972","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466234776,"identity":"179b1a35-50d3-40e0-bbd0-1999ce71411d","order_by":0,"name":"A. M. 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To enhance security, cancellable biometric transformations are employed to make it more difficult to retrieve the original biometric data. This paper introduces a new method for cancellable face recognition using Elliptic Curve Cryptography (ECC) along with selective biometric techniques. The goal of this approach is to strengthen the security of biometric traits, protect against potential attacks, and facilitate the safe transfer of biometric templates over open networks and within low-storage databases. By generating cancellable biometric patterns from the originals, this system can effectively manage access while addressing common issues associated with biometric systems, such as sensor data corruption, missing characters, under-representation, overshoot, and incompleteness. Additionally, a multi-modal biometric identification system can lower Failure-To-Capture (FTC) and Failure-To-Enroll (FTE) rates, providing strong protection against counterfeiting. 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