AI-Driven Hostel Monitoring System: Integrating Automated Attendance, Real-Time Visitor Detection, and Facial Recognition-Based Geo-Fencing Using

The existing hostel monitoring systems are based on flawed identification methods that can easily be spoofed, including GPS, Wi-Fi, RFID, or fingerprints. Our proposed approach addresses these constraints by utilizing face recognition-based geo-fencing, which will effectively and securely monitor movement inside hostel premises. Automatic attendance is implemented in real time, thereby avoiding human roll calls and maximizing efficiency. Moreover, the system can identify unauthentic people as well, hence preventing security violations through an integrated visitor detection module. Geo-fencing restricts entry and immediately informs the authorities in case of unlawful entry. The solution is highly accurate and scalable with the incorporation of hardware, such as cameras, edge computing devices, cloud storage, and real-time notifications powered by machine learning. Facial recognition and artificial intelligence make the hostels secure and efficient. In this paper, analysed the AI-based hostel monitoring systems, focusing on automatic attendance, real-time visitor detection, and face recognition-based geo-fencing. The research analyses the role of machine learning in enhancing security and efficiency in hostel management.