Geo-Location based Attendance Tracking and Face Recognition System using ResNet Algorithm

Geolocation technology captures the precise location of a user or device from GPS, Wi-Fi, or cellular geolocations in order to provide accurate tracking toward monitoring attendance. Face recognition is the biometric verification process that identifies people through matching distinctive face patterns, which allows for safe and automated authentication. A successful system of attendance monitoring depends on high parameters including identification precision, security to avert fraud and unauthorized use, and effectiveness to operate in real time. Nonetheless, the systems are hampered by shortfalls such as GPS inaccuracy from signal interference or spoofing, failure of facial recognition during low light, and susceptibility to proxy attendance fraud. To solve such problems, an integrated face recognition and geolocation system can be used to verify user identity and physical presence in an attempt to provide security and avoid attendance marking spoofing. The proposed solution here is an effective and scalable solution that improves the degree of automation, improves the accuracy of verification, and provides a more improved attendance tracking system for academic and corporate settings.