Paper Title
Face Recognition Attendance

Abstract
This paper introduces a Facial Recognition Attendance System leveraging deep learning techniques, specifically Residual Networks, to address attendance management challenges in educational institutions. Traditional attendance tracking methods, such as manual entry or barcode scanning, are often error-prone, time-consuming, and lack efficiency. The proposed system aims to streamline this process by automating attendance recording through facial recognition technology. The system's architecture is based on Residual Networks, a class of deep neural networks renowned for their ability to train deeper models without encountering the vanishing gradient problem. The use of deep learning enables the system to learn intricate facial features and patterns, resulting in high accuracy and reliability in attendance tracking. The dataset used for training and validation consists of facial images collected from students and staff, ensuring diversity and representativeness. The evaluation of the Facial Recognition Attendance System yielded promising results, with an accuracy rate surpassing 97%. The findings of this study hold significant implications for educational institutions seeking to modernize their attendance tracking systems.