Quantum Cryptography with Optical Encryption for Cancelable Biometric Systems in Crowd Control

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This preprint studies a cancelable biometric access approach for crowd control that uses optical quantum image cryptography to encrypt/deform face biometric templates to mitigate hacking risks associated with storing original biometric data. Using a correlation-based method for template comparison and identity verification, it reports simulation experiments on the FERET database with genuine versus imposter distributions, reporting Equal Error Rate (EER) and AROC values alongside claims of robustness to noise and improved performance versus other state-of-the-art algorithms. The paper’s explicit limitation is that it is a preprint that has not been peer reviewed (and is under review with a revision requested). This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract Crowd control is one of the new research trends in images and video processing. Crowds occur in cases in which so many people are allowed to enter a limited area as in Holy places, stadiums, events and demonstrations. There are two trends to deal with issue of crowds. The first trend is to use crowd analysis algorithms to analyze images or videos. The objective of this trend is to send alarms for operators or organizers to control the entry rate to crowded places. On the other hand, the second trend is to use biometrics from the beginning to limit the entry to allowed persons only. In case of using biometrics, the ethical dilemma arises of collecting user biometrics in large databases that may subject to hacking attempts. This paper presents a new trend for biometrics based on cancelable biometrics concepts to control the entry to crowded places. The objective is to use encrypted or deformed versions of people’s biometrics for the access process to avoid hacking attempts. The proposed approach depends on quantum image cryptography. The users’ face images are encrypted with quantum cryptography algorithms and saved in biometric databases. The objective of this process is twofold. First, the original templates are encrypted for privacy presentation. Second, quantum processing abilities are well-exploited to generate the templates efficiently. A correlation-based approach is exploited for template comparison and identity verification. Simulation results on FERET database, based on genuine and imposter distribution, ensure Equal Error Rate ( EER ) values of 100%, and Area Under the Receiver Operating Characteristic Curve (AROC) values of 99.51%. Moreover, experiments reveal high robustness to noise in addition to superior performance compared to other state-of-the-art algorithms.
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Abdelwahab, Prof. Fathi E. Abd El-Samie, Prof. Ashraf Khalaf, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6459961/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Crowd control is one of the new research trends in images and video processing. Crowds occur in cases in which so many people are allowed to enter a limited area as in Holy places, stadiums, events and demonstrations. There are two trends to deal with issue of crowds. The first trend is to use crowd analysis algorithms to analyze images or videos. The objective of this trend is to send alarms for operators or organizers to control the entry rate to crowded places. On the other hand, the second trend is to use biometrics from the beginning to limit the entry to allowed persons only. In case of using biometrics, the ethical dilemma arises of collecting user biometrics in large databases that may subject to hacking attempts. This paper presents a new trend for biometrics based on cancelable biometrics concepts to control the entry to crowded places. The objective is to use encrypted or deformed versions of people’s biometrics for the access process to avoid hacking attempts. The proposed approach depends on quantum image cryptography. The users’ face images are encrypted with quantum cryptography algorithms and saved in biometric databases. The objective of this process is twofold. First, the original templates are encrypted for privacy presentation. Second, quantum processing abilities are well-exploited to generate the templates efficiently. A correlation-based approach is exploited for template comparison and identity verification. Simulation results on FERET database, based on genuine and imposter distribution, ensure Equal Error Rate ( EER ) values of 100%, and Area Under the Receiver Operating Characteristic Curve (AROC) values of 99.51%. Moreover, experiments reveal high robustness to noise in addition to superior performance compared to other state-of-the-art algorithms. Physical sciences/Engineering/Electrical and electronic engineering Biological sciences/Computational biology and bioinformatics Health sciences/Biomarkers Physical sciences/Optics and photonics Security Cancelable Biometrics Quantum Image Distortion (QIP) Algorithms Face Recognition AROC Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Jul, 2025 Reviews received at journal 19 Jul, 2025 Reviews received at journal 15 Jul, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 28 Apr, 2025 Editor invited by journal 24 Apr, 2025 Submission checks completed at journal 24 Apr, 2025 First submitted to journal 16 Apr, 2025 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-6459961","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449544490,"identity":"941525a1-0bca-43db-a0ac-59877f06534e","order_by":0,"name":"Khaled M. 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