Enhanced Criminal Identification through MTCNN: Leveraging Advanced Facial Recognition Technology

Due to the rise in crime, it is harder for police to identify criminals in public. Constantly reviewing surveillance footage is a laborious process that requires careful attention to detail and is not cognitively stimulating, which increases the likelihood of mistakes. Even though many existing systems for the identification of criminals have been developed, their accuracy in recognizing them is very low. The project's goal is to use Multi-task Cascaded Convolutional Neural Networks to create an advanced criminal detection and alert system. The MTCNN model is trained using a variety of datasets that include the face features, attire, and other distinguishable traits of well-known criminals. Improving the network's capacity to identify patterns and characteristics that are essential for precise criminal identification is part of the training process. During real-time operation, the system continuously analyzes video streams from surveillance cameras, applying the trained MTCNN model to detect and identify potential criminals in public places. The utilization of MTCNN s enables the system to overcome challenges such as varying lighting conditions, partial obstructions, and changes in appearance, ensuring reliable performance in dynamic environments. Upon the detection of a potential match, the system triggers an alert mechanism, notifying law enforcement agencies in real-time. Relevant information is included in the notifications, such as the image, location, and details of the identified person. Our proposed system has the potential to enhance accuracy to 96.2 % for the identification of criminals in both public and private places.