International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Real-Time Facial Recognition-Based Attendance System with Face Liveness Detection and Multi-Role Portal Architecture |
|---|---|
| ABSTRACT | Traditional attendance management process in educational institutions is time-consuming and prone to errors, proxy attendance, and inefficiencies. This paper proposes a smart AI-driven attendance system that automates attendance using facial recognition technology. The system utilizes YOLOv8 for real-time face detection and FaceNet for accurate identity recognition through deep feature embeddings. A FastAPI-based backend handles API requests, authentication, and data processing, while a Next.js frontend provides interactive dashboards for students, faculty, and parents. The system captures images, verifies identity, and records attendance automatically without manual intervention. This contactless solution improves accuracy, enhances efficiency, and ensures secure, scalable attendance management with real-time monitoring and analytics. |
| AUTHOR | E.SIVAKRISHNA, G.LAVANYA, K.GREESHMA, V.KALPESH RAO, S.KARTHEEK, CH.VARDHAN KUMAR Assistance Professor, Department of CSE(AI&ML), Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT), Vishakhapatnam, India Students, Department of CSE(AL&ML), Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT), Vishakhapatnam, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403096 |
| pdf/96_Real-Time Facial Recognition-Based Attendance System with Face Liveness Detection and Multi-Role Portal Architecture.pdf | |
| KEYWORDS | |
| References | 1. Ms. Nivetha Renjuies.R, Mrs. Gayathri.R ,“Automated Student Monitoring and Attendance System with Faster R-CNN”, 2023. 2. Dr. S. Balaji, Mrs. S. Sugashini, J. Ruchitha, A. Tejaswini and A. Vaishnavi3,”Automated Student Monitoring and Attendance System with Faster RCNN”,2025. 3. Garv Kamra,”AI-Based Classroom Monitoring and Attendance System”,2025. 4. Prof. Shital.D Jadhav,” A Modern Web-Based Student Attendance Management System”,2025. 5. Kotramma T S and Team,”Automatic Attendance System Using Machine Learning”,2023. 6. Divy Shukla ,”AI-Based Attendance Monitoring System”, 2023. |
