Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
The Facial Recognition-Based Attendance Management System revolutionizes how attendance is tracked in schools and workplaces. By leveraging advanced facial recognition technology, it streamlines the process, eliminating errors and inefficiencies found in traditional methods. Using computer vision and machine learning, the system accurately records attendance in real-time, preventing fraudulent entries and ensuring reliability. With minimal manual effort required, it simplifies tasks for both administrators and users. Its flexibility and scalability make it a perfect fit for various settings. Future upgrades may introduce additional biometric options and stronger security measures to protect user data
Keywords:
Face Recognition, Attendance Automation, Computer Vision, Machine Learning, Convolutional Neural Network, Real-Time Processing, OpenCV, LBPH, Haar Cascade.
Cite Article:
"Smart Attendance:Automated Facial Recognition System", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.a188-a191, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503022.pdf
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000545
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator