Real Time Facial Recognition-Based Criminal Identification Using MTCNN

This work presents a novel approach to real-time criminal detection through the use of cutting-edge face recognition technology. Accuracy and Reliability, Scalability, Environmental Variability, Camera Quality and Resource Constraints are the major challenges faced by this problem. In order to improve public safety and support law enforcement, the system uses the Multi-Task Cascade Neural Network (MTCNN) to reliably identify and recognize faces in difficult situations, such as low light or obscured views. Due to MTCNN's strong deep learning capabilities, people of interest may be quickly identified and perhaps prevented from committing crimes in busy or dimly light settings with high accuracy identification. Even with few reference photos, the technology can match identified faces to a database of known criminals, guaranteeing flexibility in a range of scenarios. One of its primary features is its 90% accuracy in real-time analysis of live video feeds from security cameras, which facilitates quick reactions to any threats and improves community safety. This technology, which combines face detection, identification, and real-time processing, is a major step forward for law enforcement in their fight against crime and for maintaining community security.