Face Recognition-Based Criminal Detection: Integrating Image Databases with Live Video Surveillance

The Criminal Detection Platform leverages cutting-edge video surveillance and face recognition technology to revolutionize public safety and law enforcement operations. This system integrates deep learning algorithms to process live or recorded video feeds, identifying and verifying individuals against a pre-existing database of known offenders. By automating the surveillance process, the platform minimizes human effort, enhances the speed and accuracy of criminal identification, and provides real-time alerts to authorities. The platform is designed to perform reliably in difficult circumstances like dim lighting and partial occlusions and high crowd density, ensuring consistent performance across diverse environments. Convolutional neural networks (CNNs) and other sophisticated neural network topologies, power the face recognition process, achieving high precision in detecting and matching facial features. To accommodate large-scale deployments, the system supports scalability for handling multiple video streams simultaneously. Ethical and legal considerations are at the core of the platform’s design, with robust data protection mechanisms to ensure compliance with privacy laws and prevent misuse. By focusing on both technical excellence and responsible implementation. The study shows how to update surveillance systems in a progressive manner.