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This paper describes a novel attendance system that uses facial recognition technology to automate attendance-taking in classrooms or workplaces. Instead of manually recording attendance, the system detects and identifies individuals in real-time by analyzing their facial features using computer vision and machine learning techniques. The system is designed to be fast and accurate and can handle variations in lighting, pose, and facial expressions. It uses advanced deep-learning models for facial recognition and has been proven to achieve high accuracy rates in identifying faces and recording attendance. The proposed system has the potential to streamline administrative processes and increase efficiency in various settings, including schools, universities, and workplaces.
Keywords:
Face Recognition, Face Detection, Convolution Neural Network, Haar Classifier, Cascading Classifier, Feature Extraction.
Cite Article:
"Face Recognition Based Attendance System Using Machine Learning Algorithms", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 1, page no.751 - 756, January-2024, Available :http://www.ijrti.org/papers/IJRTI2401111.pdf
Downloads:
000205281
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