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 | Face recognition based attendance Using Artificial Intelligence, Computer Vision, Deep Learning, OpenCV, and Python |
|---|---|
| ABSTRACT | The Face Recognition Based Attendance System is an advanced solution designed to automate the process of attendance marking using Artificial Intelligence and Computer Vision techniques. Traditional attendance systems are time-consuming, prone to errors, and allow proxy attendance, which reduces reliability. To overcome these issues, this system uses a camera to capture real-time facial images and compares them with stored data in a database for accurate identification. The system follows a structured approach including data collection, face detection, feature extraction, and face recognition. Facial features are analyzed using deep learning models to ensure accurate and efficient identification of individuals. Once a match is found, the system automatically records attendance along with date and time, reducing manual effort and increasing efficiency. |
| AUTHOR | SIMRAN PAWALE, KRUSHNA SHINDE, JIGNESH NAIK, VIRAJ DEVEKAR, A.A. SHIRODE Department of Artificial Intelligence & Machine Learning, AISSMS Polytechnic, Pune, Maharashtra, India Guide, Department of Artificial Intelligence & Machine Learning, AISSMS Polytechnic, Pune, Maharashtra, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404037 |
| pdf/37_Face recognition based attendance Using Artificial Intelligence, Computer Vision, Deep Learning, OpenCV, and Python.pdf | |
| KEYWORDS | |
| References | 1. Artificial Intelligence: A Modern Approach, Pearson Education, 3rd Edition. 2. Digital Image Processing, Pearson Education. 3. OpenCV Library Documentation, Available at: https://opencv.org 4. Face Recognition Library Documentation, Available at: https://github.com/ageitgey/face_recognition 5. Research Paper: “Face Recognition Based Attendance System using Machine Learning”, available on IEEE Xplore Digital Library. 6. Research Paper: “Automated Attendance System using Face Detection and Recognition”, available on Springer. |
