Project Overview

I developed an advanced attendance management app for my university that integrates geolocation and facial recognition technologies to automate and enhance the accuracy of attendance tracking. The app utilizes GPS geofencing to automatically record attendance when students are within the designated campus boundaries, ensuring that only those physically present are marked as attending. This eliminates the need for manual check-ins and prevents location-based attendance fraud. Additionally, the app features facial recognition technology, allowing instructors to capture a classroom photo and automatically detect and identify students in real time. Powered by PyTorch, the facial recognition system cross-references the detected faces with the class roster and records the attendance instantly. This dual approach—geolocation for remote verification and facial recognition for in-class attendance—streamlines the process, reduces administrative overhead, and improves both accuracy and efficiency. The app is built using React Native for cross-platform compatibility, PyTorch for advanced facial recognition, Google Maps API for location tracking, and Firebase for real-time data storage and management. This project demonstrates my ability to blend cutting-edge AI techniques with backend technologies to solve real-world challenges, particularly in educational settings.