A Multi-Camera Automated Attendance System Using LBPH-Based Face Recognition
DOI:
https://doi.org/10.70619/vol5iss12pp18-29-694Keywords:
Face detection, Feature Extraction, Face Recognition, LBPH, Haar Cascade algorithms, Python, OpenCV, IP CameraAbstract
This study presents the development and evaluation of a Multi-Camera Automated Attendance System employing Local Binary Patterns Histograms (LBPH) for face recognition. Traditional single-camera solutions struggle in large classrooms with occlusion, lighting variability, and limited viewpoints. All these challenges are addressed by the Multi-Camera Automated Attendance System Using LBPH-Based Face Recognition (MCAAS-LBPH). This system integrates three IP cameras with LBPH and Haar Cascade Classifier algorithms to achieve real-time, accurate student identification. It provides daily reporting and stores attendance in Comma Separated Value (CSV) format for quick conversion to Excel or PDF.
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Copyright (c) 2025 Nelson Habumugisha, Jonathan Ngugi, Djuma Sumbiri
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
