Performance Evaluation of Face Mask Detection Using Feature Descriptor and Supervised Learning Method
DOI:
https://doi.org/10.30871/jaic.v10i1.11999Keywords:
Face Mask Detection, Feature Extraction, LBP, Machine Learning, Random ForestAbstract
The use of masks as a measure to prevent the spread of dangerous diseases such as COVID-19 and others has become a social norm. Manual detection is less effective, especially in areas with high mobility. This study develops and evaluates an artificial intelligence (AI)-based face mask detection system using feature description and machine learning models. An optimal and lightweight model can help hospitals implement face mask detection systems in areas prone to disease transmission. Image preprocessing, feature description, supervised learning model studies, and performance evaluation were conducted using accuracy, precision, recall, and F1-score metrics, and a confusion matrix was used to assess the overall model performance. The performance evaluation results show that the combination of the LBP feature description with the random forest model is the best choice, with a relatively high and stable accuracy of around 96.3% with an average value, precision, recall, and F1-score of around 96% using K-Fold Cross-Validation. These findings suggest that this method is helpful in detecting mask use while minimizing error and computation rates. This study contributes to the development of lightweight mask detection systems that can be used in real time.
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Copyright (c) 2026 Adi Suheryadi, Faisal Dharma Adhinata, Heru Wijanarko
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