Advanced Facial Recognition And OTP Verification In High-Volume POS Transactions
Oliver Obilor Osuagwu1
1Department of Software Engineering, South Eastern College of computer Engineering and information Technology, Egbu, Owerri, Nigeria.
Abstract: Financial security has become a critical global concern amid the rapid expansion of automated banking services, digital transactions, and highly interconnected financial infrastructures. Automated Teller Machines (ATMs) and Point-of-Sale (POS) terminals provide essential 24/7 access to cash and financial services, yet they remain prime targets for fraud, including card skimming, identity theft, and unauthorized account access. This survey examines the evolution of ATM authentication mechanisms, tracing the shift from traditional token‑based methods (card and PIN) to advanced biometric‑driven approaches.
The study presents an in‑depth review of a multifactor authentication framework that integrates Deep Learning–based facial recognition—leveraging Convolutional Neural Networks (CNNs) and FaceNet—with dynamic One‑Time Password (OTP) verification. Core components such as facial feature extraction, liveness detection, and secure data transmission protocols are analyzed. The performance of the proposed biometric model is compared with legacy authentication systems using metrics such as security robustness, processing latency, and user experience.
Additionally, the survey highlights persistent challenges, including sensitivity to lighting variations, occlusion issues, and network‑induced delays. Emerging research directions—such as behavioral biometrics, federated learning, and Edge AI for real‑time processing—are explored to illustrate future opportunities for enhancing ATM security. By synthesizing current trends and technological advancements, this paper provides a comprehensive understanding of biometric authentication for ATMs and underscores its significance in mitigating financial fraud
Conclusion: The rapid growth of digital banking in Nigeria has amplified both the convenience and the vulnerabilities of ATM and POS‑based financial transactions. While these platforms have expanded financial inclusion, they have also become hotspots for sophisticated fraud schemes. Recent cases across major Nigerian cities highlight recurring patterns: POS agents colluding with criminals to clone cards, ATM terminals compromised with skimmers and hidden cameras, and social‑engineering attacks where unsuspecting users are deceived into revealing PINs or surrendering their cards. Incidents such as coordinated ATM jackpotting attempts in Lagos and the surge of POS‑related identity theft in states like Abuja, Rivers, and Oyo underscore the urgency of strengthening authentication mechanisms.
Key Word: ATM security, Biometric authentication, Facial recognition, Deep learning, Convolutional Neural Networks (CNNs), FaceNet, Multifactor authentication, One-Time Password (OTP), Liveness detection, Financial fraud prevention, Secure data transmission, Behavioral biometrics, Edge AI, Authentication latency, Identity theft mitigation
Advanced-Facial-Recognition-And-OTP-Verification