AI Vision: Advanced Deep Learning for Face Recognition

The traditional approach to record attendance is performed manually by either the teacher or the administrator, necessitating an extensive amount of time and effort, and sometimes resulting in errors and instances of proxy attendance. With the growing student population, universities and institutions face the challenging burden of effectively monitoring and managing student records. This paper presents a new face recognition attendance system based on Convolutional Neural Networks (CNNs), which have shown significant progress in the field of face detection and recognition. The suggested solution uses cutting-edge face recognition technique to automatically recognize and validate individuals. To ensure accuracy and dependability, a Convolutional Neural Network (CNN) model was trained and tested on the Face Recognition dataset. The proposed approach achieved 97.88% recognition accuracy. The technology demonstrates its ability to effectively serve as an alternative for the Face Recognition technology. Future development will focus on enhancing the system’s scalability and incorporating other functionalities such as attendance records and real-time deployment.