THE REAL TIME HIGH PRECISION CRIMINAL FACE RECOGNITION FOR ATM SECURITY IN DEEP LEARNING

Abstract

The proposed ATM security system, utilizing composable deep face recognition, emphasizes user protection during transactions with a specific focus on eye retina accuracy. In the event of a potential threat identified through real-time face detection and tracking, an automatic alert is initiated. Prioritizing precision, the system employs advanced face detection algorithms for accurate location and tracking, even in dynamic scenarios. A down sampling technique ensures efficient processing of facial data, crucial for meticulous identification in crowded ATM locations. The integration of a face tracking ID unit enhances accuracy, providing continuous monitoring and persistence in identification, particularly focusing on the distinctive patterns in the eye retina. The scoring method, rooted in face tracking and embedding distance, significantly elevates user identification reliability, minimizing the risk of false positives, especially in criminal identification based on unique eye retina features. In response to potential threats, the system promptly generates automatic alerts and dispatches secure emails to relevant entities, including the affected card user, the respective bank, and cybersecurity authorities. These emails include comprehensive information, such as a high-precision image of the detected criminal's face, detailed facial recognition data, and a thorough summary of their known criminal history. This accuracy-driven and retina focused approach enhances safety, emphasizing continuous refinement and adherence to privacy and ethical considerations during implementation. KEYWORDS: Crime prevention, down-sampling, face recognition.