Abstract:
This thesis describes how facial recognition and location tracking technologies were used to create an effective
web-based attendance application. The primary contribution of this project is the reduction of presence
manipulation through the combination of geolocation and advanced face biometric verification to provide safe
and accurate attendance records. This is accomplished by merging GPS-based position tracking to verify the
existence of people with real-time face recognition powered by machine learning algorithms.
A thorough assessment was conducted to determine the efficacy of the suggested system in a variety of settings
and circumstances (poor illumination, different faces, and both inside and outside the designated area). The
accuracy and user satisfaction of the application were evaluated in order to evaluate its performance. It was
discovered that the system tracked locations with an error margin of less than 5 meters and recognized faces with
a precision rate of 75%.
The findings show that the suggested application greatly improves the dependability and effectiveness of
attendance tracking systems. Institutions and organizations looking for a smooth, automated solution for
attendance tracking may find this modeling method especially helpful.
