An Automated University Gate Pass Monitoring System Using Deep Learning

Surveillance cameras are necessary to find and track a person in the technology world for various security purposes. The work aims to design an auto gate pass system using face recognition technology based on real-time video processing. Various photographs of the student at different environmental conditions have been collected and stored in the database. While exiting from the campus students need to show their face in front of the camera and the proposed system recognizes the student by comparing the details stored in the database. After recognizing a student, the next step is to fetch the student's record from the database and make the entry of a person into e-register. Meanwhile, the automated system will send a notification to the respective student. To track the whole action, the system provides a graphical user interface (GUI), which helps to monitor all activities more easily.