Abstract
In the world, a countless number of people including kids, teens, old-aged people are missing every day. Most of them remain untraced. Tragically, the majority of these cases remain unresolved. Missing persons face numerous dangers, with some becoming victims of death, rape, or abuse. The uncertainty surrounding their fate exposes concerned individuals such as parents, friends, relatives, and guardians to significant stress and worry. This study proposes a system designed to aid both the police and the public by accelerating the search process through the implementation of face recognition technology. A comparative study conducted with other models, including MTCNN, SSD, and Haar Cascades, demonstrates YOLOv8's superior real-time performance and accuracy. The system incorporates a collaborative public-police alert mechanism and offers a scalable solution suitable for implementation in large-scale urban environments. Testing of the system reveals a recognition accuracy of 94% with a detection time of approximately 40 milliseconds per frame.
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