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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

Exploring facial biometrics in multi-factor authentication systems for secure banking applications

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pp. 655–662Vol. 29Issue 2-AFebruary 2026DOI: 10.47974/JDMSC-2508 Crossmark XML
Received:
09 Apr 2025
Published Online:
07 Feb 2026
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2508
Pages:
655–662

Abstract

Due to convenience, as well as its security, facial recognition is fast becoming an important component of multi-factor authentication (MFA) for biometric authentication, especially banking. So, while centralizing the training of face recognition models presents privacy risks because large stores of sensitive facial data become vulnerable to leaks, today’s use cases are best served by the centralized approach. In this paper, we propose a facial recognition system based on federated learning for MFA that protects user privacy by keeping user biometrics on device. To extract robust face features, we train in a federated manner by using a deep residual network (ResNet 100) backbone across distributed clients without sharing raw images. These liveness detection capabilities are incorporated into, and evaluated against, presentation attacks using the CelebASpoof face anti spoofing dataset (625,537 images, 10,177 subjects). We show experimental results demonstrating that our proposed federated model can attain 98.2 % authentication accuracy with 1.3% Equal Error Rate (EER), which exceeds that of both a trained centralized CNN (95.6% accuracy and 3.4% EER) and a cloud based FaceNet baseline. Importantly, our method does not come at the expense of accuracy as we manage to reduce privacy risks by orders of magnitude. Based on this work we conclude that, by using federated learning, we can achieve highly accurate and privacy preserving face recognition for secure banking authentication.

Keywords

Mathematical modelingOrdinary differential equationsA duopoly economyMarket share

Subject Classifications

Primary 93A30Secondary 49K15

References

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