ST604: FACE RECOGNITION FOR I-MARS APPLICATION ACCESS UNDER DYNAMIC LIGHTING CONDITIONS

Izz Haziq Mohd Bin. Mohd Kamal Universiti Pertahanan Nasional Malaysia

The project delves into the critical domain of weapon storage systems, focusing specifically on the armaments rack utilized by the Malaysian Armed Forces (MAF). Recent security incidents, such as the attempted intrusion at the 4th Battalion Border Regiment (4 RS) in Gerik, Perak, highlight the urgent need for an upgraded storage system to efficiently manage weapons. This underscores the vital role of weapons in safeguarding military bases, residents, and classified information. The problem statement centers on the necessity for an enhanced storage system that aligns with the operational requirements of the MAF. The research methodology encompasses a comprehensive approach, including an in-depth literature review, case studies, and interviews with military personnel, aimed at understanding current practices and challenges. Initial findings emphasize the significance of proper weapon storage and its direct impact on security outcomes. In line with research recommendations, the study advocates for the adoption of advanced armaments rack designs integrating technological features to enhance security and accessibility. These insights contribute to the broader discussion on military infrastructure and offer a guiding framework for the MAF to optimize weapon storage practices for heightened efficiency and security, addressing the diverse needs of its branches. Moreover, the study acknowledges the impact of dynamic lighting situations on the face recognition process within the Intelligent Modernized Armaments Rack System (i-MARS) application. Given the variable lighting conditions in operational environments, the effectiveness of facial recognition algorithms may fluctuate, necessitating robust solutions to ensure reliable performance under varying lighting scenarios. As such, considerations for dynamic lighting conditions form a critical aspect of the research, aiming to enhance the accuracy and effectiveness of facial recognition capabilities within i-MARS.