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This paper presents a Flask-based web application that combines attendance management and people counting functionalities using facial recognition and machine learning. The attendance tracking system employs the K-Nearest Neighbors algorithm to recognize faces from a webcam feed, automatically logging attendance data in a CSV file. The people counting component recognizes individuals' heads from video feeds to count the number of people passing through entryways, enabling real-time monitoring for use cases like emergency evacuations and crowd control. The integrated application streamlines attendance tracking, provides a responsive administrator interface, and ensures data persistence through CSV files and serialized facial recognition models. The combination of these techniques in a user-friendly web interface makes this application a powerful tool for educational institutions, organizations, and public facilities.
Keywords:
Attendance Management, Facial Recognition, Machine Learning, Flask, Web Application
Cite Article:
"Automated Attendance Sentinel", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 4, page no.379 - 387, April-2024, Available :http://www.ijrti.org/papers/IJRTI2404054.pdf
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000205230
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