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 | Attendance Using Face Recognition System a Comprehensive Literature Survey |
|---|---|
| ABSTRACT | Face recognition-based attendance systems are an advanced application of Artificial Intelligence and Computer Vision that automate attendance processes. Face recognition-based attendance systems are an advanced application of Artificial Intelligence and Computer Vision that automate attendance processes. Face recognition-based attendance systems are an advanced application of Artificial Intelligence and Computer Vision that automate attendance processes. This literature survey focuses on the use of Artificial Intelligence (AI) and Computer Vision techniques in attendance management systems using face recognition. Traditional attendance methods such as manual registers and biometric systems are time-consuming and prone to errors . Face recognition provides a contactless, efficient, and automated solution. This paper reviews various face detection and recognition techniques including Haar Cascade, Local Binary Pattern Histogram (LBPH), and deep learning models such as Convolutional Neural Networks (CNN). It also explores the use of OpenCV and machine learning algorithms for real- time attendance tracking. The survey identifies challenges such as lighting conditions, pose variations, and data privacy issues. The findings help in developing a smart attendance system that improves accuracy, |
| AUTHOR | KRUSHNA SHINDE, SIMRAN POWALE, JIGNESH NAIK, VIRAJ DIVEKAR Department of Artificial Intelligence and Machine Learning, AISSMS Polytechnic, Pune, Maharashtra, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404023 |
| pdf/23_Attendance Using Face Recognition System a Comprehensive Literature Survey.pdf | |
| KEYWORDS | |
| References | 1. Viola, P., & Jones, M. (2001). Rapid Object Detection using Haar Cascade. Turk, M., & Pentland, A. (1991). Face Recognition using Eigenfaces. 2. Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face Recognition with LBPH. OpenCV Documentation - Face Recognition. 3. Taigman, Y. et al. (2014). DeepFace: Closing the gap to human-level performance. |
