Robust Student Attendance Checking System Using Efficient LBP-based Ensemble Learning Approaches
Vinh Dinh Nguyen, Le Huy Hoang, Nguyen Thi My Ai, Nguyen Huy Khanh, Tran Dai Loi, Huynh Hung Loi · 2025
Facial recognition systems often face challenges in balancing accuracy and computational efficiency, particularly in real-world environments with varying lighting conditions and image quality.Many existing methods rely on individual machine learning models, which may not fully capture the complex patterns needed for high-precision recognition across diverse datasets.This research addresses these limitations by implementing and comparing several machine learning algorithms-K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM)-and improving their performance through ensemble techniques such as Voting and Stacking.The process includes key steps like image preprocessing, facial detection using "hog" and "cnn" models, and feature extraction with facial encodings and Local Binary Pattern (LBP).Additionally, extensive data augmentation techniques-including image rotation, noise injection, LBP, and brightness adjustment-were applied to simulate real-world variations and improve model robustness.Dimensionality reduction with principal component analysis(PCA) and hyperparameter tuning via GridSearchCV further optimized performance.Our proposed method achieved a facial recognition accuracy of 99.22%, exceeding the results of the SVM 95.34% [15].This demonstrates the effectiveness of integrating machine learning models and applying ensemble methods, leading to more reliable facial recognition systems and marking a significant improvement over current approaches.