Yolo-Based Biometric Systems for Online Banking and Mobile Authentication: Implementation, Evaluation, Ablation Study and Comparison with Zoloz

Abstract

The fast growth of online banking and mobile financial services has heightened the demand for secure, easy-to-use authentication mechanisms. Traditional methods like passwords and one-time passwords are increasingly susceptible to cyber-attacks, which constitutes the main motivation towards the adoption of biometric-based authentications. From biometric modalities, face recognition has gained widespread acceptance owing to its non-intrusive nature and suitability for mobile devices. Recent deep learning advancements have made real-time face detection and recognition possible via object detection frameworks like YOLO (You Only Look Once). This work presents a comprehensive analysis of a YOLO-based biometric authentication system devised for online banking and mobile applications. This paper proposes a complete biometric pipeline that uses YOLO for face detection and deep embedding-based models for recognition. Face detection performance is evaluated on the WIDER FACE dataset, while recognition accuracy is assessed on the LFW dataset. The paper presents a reproducible implementation in detail through a Google Colab environment. System performance is analyzed in terms of detection accuracy, recognition accuracy, inference speed, and end-to-end latency. An extensive ablation study investigates the impact of key components, including detection architectures, face alignment strategies, embedding model selection, and similarity threshold tuning. Furthermore, the proposed research framework is compared against Zoloz, a commercial enterprise-grade biometric authentication platform widely adopted in the banking sector. The results show that YOLO-based biometric systems are very effective for research and prototyping, while real-world banking deployment requires additional security, compliance, and robustness considerations.