This project was developed using Python and leveraged the face_recognition library for facial recognition capabilities. The primary goal of this project was to create an efficient and user-friendly system for managing attendance records using facial recognition technology.
Key features of the project included:
Face Recognition: The system used the face_recognition library to recognize and verify the identity of students based on their facial features.
Attendance Management: The system could capture and process images from a camera or image source, identify individuals, and mark their attendance automatically. This streamlined the attendance-taking process.
Firebase Integration: Firebase, a popular mobile and web application development platform, was utilized for two main purposes:
Real-time Database: Firebase Realtime Database was used to store student information and attendance records. This allowed for easy and instant updates to attendance data.
Firebase Storage: Firebase Storage was employed to securely store the dataset images used for facial recognition training. User-Friendly Interface: The project includes a user-friendly interface, making it easy to manage attendance records and view individual attendance histories.
The "Face Recognition Attendance Management System" is a valuable application of machine learning in a practical context, enhancing the efficiency and accuracy of attendance tracking while maintaining data security and user convenience. It represents a significant step forward in automating routine administrative tasks and demonstrates the potential of machine learning to improve various aspects of education and institutional management.