Published Paper Details:

Attendance management is a fundamental task in educational institutions and workplaces, but traditional methods, such as roll-calling or card- swapping, are prone to inefficiencies, inaccuracies, and time wastage. Keeping track of attendance the traditional way can often lead to mistakes and even manipulation. To solve this, our paper introduces a smarter approach--a Face Recognition-Based Attendance System. By using advanced computer vision and machine learning, this system makes attendance tracking more accurate, efficient, and hassle-free. The system utilizes OpenCV and dlib for detecting real-time face and recognition, enabling a seamless and contactless way to mark attendance. Our system uses the ResNet50 model to capture unique facial features, creating 128-dimensional embeddings to accurately match individuals with stored records. Attendance is securely logged in an SQLite database, and a simple Flask-based web interface makes it easy to access records anytime. By automating the process, this system removes the hassle of manual entry, reduces errors, and provides a reliable solution for schools, colleges, and workplaces. Additionally, the integration of Tkinter for the face registration interface ensures ease of use, while SQLite offers a reliable storage system. By implementing this solution, administrative workload is significantly reduced, accuracy is enhanced, and attendance management becomes more streamlined and efficient.