Published Paper Details:

The human face serves as a fundamental and unique identifier, especially in the context of criminal detection and law enforcement. The increasing challenges posed by highly populous urban environments demand automated solutions for efficient and timely identification of individuals. This research introduces an innovative real-time criminal identification system that integrates deep learning, specifically Convolutional Neural Networks (CNN), and Haar Cascade classifier for face detection and recognition. The system utilizes live camera feeds in urban environments, enhancing law enforcement capabilities by combining facial recognition with historical criminal activity data. The proposed approach focuses on extracting detailed facial features through CNN, ensuring robust detection in challenging scenarios. The integration of Haar Cascade enables high-precision real-time face detection. our research contributes to the advancement of criminal identification systems by introducing a real-time approach that harnesses the power of deep learning and live camera feeds. The proposed system holds significant potential for enhancing law enforcement capabilities, enabling proactive identification, and contributing to the overall safety and security of urban environments. As a forward-looking solution, our research not only addresses current challenges but also anticipates future needs in the ongoing evolution of urban security. By incorporating real-time data analytics, our system aids authorities in making informed decisions, reinforcing its role as a proactive and intelligence-driven asset for ensuring public safety.