Implementation of Automatic Attendance System Based on Face Recognition Using CNN Method in IPB University Vocational School Environment
Year: 2024
Type: article
Abstract: The research focuses on creating an automated attendance system using face recognition through the Convolutional Neural Network (CNN) approach at IPB University's Vocational School. The current manual attendance methods show limitations, such as potential inaccuracies in recording and the risk of cheating, like attendance proxies. To overcome these challenges, this study applies the CNN approach with Python and OpenCV, enabling automatic face detection and recognition for students. The system accurately logs attendance by identifying faces in real time. Testing indicates that the system records attendance reliably, whether with a single individual or with multiple faces present within a single frame.
Authors: Dini Nurul Azizah, Raisa Mutia Thahir, Luthfi Dika Chandra, Naufal Ardhani, Endang Purnama Giri, Gema Parasti Mindara
Institution: IPB University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4404120828