An Intelligent Facial Recognition System for Missing and Criminal Person Identification

The rising numbers of missing persons and criminal activities show the need for having smart and automated systems that can keep an eye on things. This paper discusses an Intelligent Surveillance System for Missing Persons and Criminal Recognition, which uses advanced computer vision and deep learning technologies. The proposed Intelligent Surveillance System for Missing Persons and Criminal Recognition uses OpenCV to detect, process, recognize, and classify faces in real-time from the videos received through the camera or images uploaded through the application. It uses the Convolutional Neural Network (CNN) face recognition model, ResNet-34, in combination with OpenCV. The faces that are detected and recognized through the application are aligned and normalized before they are compared with the data in the database,which contains information on missing persons and criminals. The application is created using the web application development framework Django, ensuring that the data remains secure, the records are efficiently managed, and the application remains easily accessible to authorized persons. This model has been trained to maintain high accuracy in face recognition even when the lighting, angle of the face, and quality of the image vary. As soon as the face is successfully recognized, the application sends real-time messages and displays the personal information of the person to the concerned law enforcement agencies. The experiment proves that the proposed framework is highly accurate and can be applied in real-time since it consumes less computational power, thus becoming an efficient way of preventing crime and locating.