Innovative Approaches to Criminal Identification Using Real Time Facial Recognition
Suram Divya, T Jeevika, Allin Geo · 2025
This research proposes a novel system for real-time criminal identification that makes use of OpenCV and YOLOv8 for face detection and recognition, and integrates it with Django for efficient web-based administration. Enhancing surveillance capabilities, the technology tries to recognize and identify criminal faces in live video feeds using powerful computer vision algorithms. A state-of-the-art object identification model called YOLOv8 is used for precise face recognition, with the help of OpenCV for video frame collection and processing. The trained model uses a large dataset to identify and name people, and it does so in real-time by drawing bounding boxes around faces it detects. So that the web interface and video processing can communicate without any hitches, the Django framework takes care of the backend features like data storage, user authentication, and real-time updates. By working together, these systems help law enforcement with both investigating and preventing crimes, as well as with quickly identifying already-convicted offenders. A crucial component of contemporary security systems, the system's design encourages scalability and efficiency. Aiming to improve public safety and criminal deterrent by automating the detection process, it seeks to decrease reaction times in critical circumstances.