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Journal of Discrete Mathematical Sciences and Cryptography cover

Journal of Discrete Mathematical Sciences and Cryptography

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

CiteScore 2025: 2.7 (Q1)

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Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

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Journal of Discrete Mathematical Sciences and Cryptography
Open Access Research Article

Leveraging facial recognition and AI for enhanced biometric security systems in urban surveillance

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pp. 647–654Vol. 29Issue 2-AFebruary 2026DOI: 10.47974/JDMSC-2507 Crossmark XML
Received:
13 May 2025
Published Online:
31 Dec 2025
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2507
Pages:
647–654

Abstract

Facial recognition software is becoming a staple of biometric security in public spaces whose urban surveillance systems grow ever more reliant on the technology. However, in unconstrained city environments provided in these datasets, illumination, occlusions, extreme pose and low-resolution imagery are obstacles. In this paper we propose an ArcFace-based facial recognition framework as a means to improve security and reliability for urban surveillance. We extract robust facial features using a ResNet-100 deep backbone and also use additive angular margin loss (ArcFace) to maximize inter class separability and intra class compactness. The performance of the ArcFace enhanced model is examined against state-of-the-art models (VGGFace and FaceNet) and shown to improve accuracy across identification, precision, recall, and F1 score, and lower Equal Error Rate (EER) overall. Experiments reveal that the proposed method, which significantly enhances biometric identification reliability, is able to achieve state-of-the-art recognition accuracy in such difficult surveillance conditions. Going forward, this approach can be extended to handle occluded (masked) faces and will be applied to resource constrained edge deployments. Our study shows, through experiments, the efficacy of applying ArcFace based deep learning towards the enhancement of biometric security in smart city surveillance applications. 

Keywords

Face recognitionUrban surveillanceBiometric securityArcFaceDeep learning

Subject Classifications

Primary 93A30Secondary 49K15

References

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