Design and Implementation of an Embedded System for Vehicle Anti-Theft Using Face Recognition and Live Location Tracking on Raspberry Pi

Design and Implementation of an Embedded System for Vehicle Anti-Theft Using Face Recognition and Live Location Tracking on Raspberry Pi | 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 Design and Implementation of an Embedded System for Vehicle Anti-Theft Using Face Recognition and Live Location Tracking on Raspberry Pi Ayman Elshenawy, Khalil M. Abdelnaby, Mohamed A. Rohaim, Ayman Mohamed, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8964937/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract The rapid advancement of Information Technology, which has driven down vehicle prices, has led to a substantial increase in global vehicle sales over the past two decades. However, this rise in vehicle ownership has also been accompanied by a notable surge in car theft, creating a significant challenge for vehicle owners. As a result, a continuous battle exists between vehicle manufacturers and thieves. Unfortunately, traditional security methods often fall short when facing increasingly sophisticated vehicle theft techniques, which leads to the vehicle theft problem becoming a global issue. This paper proposes an Intelligent Vehicle Anti-Theft System (IVATS) as a solution for the vehicle theft problem. The proposed IVATS integrates GSM, GPS, Raspberry Pi, and a novel Challenge-Response Face Authentication model to improve the system security. The IVATS addresses limitations of existing solutions, such as reactive responses, unreliable user verification, and poor scalability, by integrating real-time tracking, remote mobile control, and multi-layered biometric authentication. The research contribution is threefold: (1) a high security framework leveraging IoT and embedded systems, (2) a challenge-response face authentication model incorporating emotion verification to deter spoofing, and (3) a user-friendly mobile app for remote monitoring. The IVATS employs a Raspberry Pi 3 and ATmega32 microcontroller to manage hardware modules (camera, GPS, GSM) and software components, including a Horizontal Ensemble Best N-Losses (HEBNL) model, which is a fine-tuned VGG16 model for emotion recognition. The challenge-response face authentication model achieves an average accuracy of 98.89% under optimal lighting but may degrade in low-light conditions and can authenticate users in 24 ms, which is suitable for the hardware devices that constitute the system. The integration of CRFA with FER outperforms traditional VATS by enabling dynamic, liveness-aware authentication based on real-time emotional responses, which substantially mitigates spoofing and replay attacks while preserving low-latency performance on embedded platforms. Moreover, IVATS offers a practical and efficient solution for modern vehicle security by effectively balancing strong robustness with user convenience. Vehicle Security Intelligent Vehicle Anti-Theft System Mobile Control & Tracking Keyless Entry Embedded Systems Raspberry Pi ATmega32 IoT VGG16 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviews received at journal 14 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 13 Mar, 2026 Editor invited by journal 09 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 25 Feb, 2026 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. 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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-8964937","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606338130,"identity":"d799602a-633d-40bd-bf85-3f4029340f8b","order_by":0,"name":"Ayman Elshenawy","email":"","orcid":"","institution":"Al-Ahliyya Amman University","correspondingAuthor":false,"prefix":"","firstName":"Ayman","middleName":"","lastName":"Elshenawy","suffix":""},{"id":606338132,"identity":"2bf64477-7df5-4046-9611-9823534a47af","order_by":1,"name":"Khalil M. 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