Face Recognition System in Aviation Workplaces

Frequent and disorganized shift changes in aviation workplaces, particularly in airbases, present significant challenges to operational efficiency, personnel accountability, and overall quality control. The lack of a structured handover process not only hampers productivity but also poses risks to safety and documentation accuracy, especially in high-security, fast-paced environments. Traditional attendance and monitoring systems, which rely heavily on manual inputs, are prone to errors and manipulation, making them insufficient for the demands of modern aviation operations. This paper proposes the integration of facial recognition technology into airbase sections as a transformative solution to personnel tracking and shift management. By deploying camera-based systems that detect and identify individuals in real time, the system captures essential data such as personnel identity, section presence, and timestamp automatically and relays it to the central quality management system. This not only streamlines the shift change process but also enhances traceability and accountability across aviation units. The proposed system utilizes machine learning algorithms to ensure high recognition accuracy and robust data protection, forming a digital infrastructure for transparent and efficient workforce monitoring. This innovative approach redefines quality assurance practices in aviation environments, offering a scalable and automated solution to a long-standing organizational issue.