Comparative Analysis of Face Mask Detection using Lightweight CNN and Bag of Visual Word-based Classifier for Real-Time Surveillance

Ika  Candradewi; Bakhtiar Aldino Ardi S; Agus Harjoko; Andi Dharmawan

doi:10.52436/1.jutif.2026.7.1.4461

Authors

Ika Candradewi Electronics and Instrumentation, Universitas Gadjah Mada, Indonesia
Bakhtiar Aldino Ardi S Electronics and Instrumentation, Universitas Gadjah Mada, Indonesia
Agus Harjoko Electronics and Instrumentation, Universitas Gadjah Mada, Indonesia
Andi Dharmawan Electronics and Instrumentation, Universitas Gadjah Mada, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.1.4461

Keywords:

Bag of Visual Word, Convolutional Neural Network, Face Mask Detection, Multi-layer Perceptron

Abstract

Face mask detection has become increasingly important across various sectors, including healthcare, food processing industries, and public safety, to ensure adherence to health and hygiene protocols and minimize the risks of contamination. Manual supervision of mask usage is often inefficient, labor-intensive, and prone to inconsistency. To address this challenge, this study proposes an automated face mask detection system utilizing computer vision technology, designed for real-time monitoring on resource-limited edge devices, such as the Raspberry Pi 4 with a Google Coral USB Accelerator.

The system integrates Multi-task Cascaded Convolutional Neural Networks (MTCNN) for face detection and a modified lightweight Convolutional Neural Network (CNN) for binary mask classification. Deployed via a web-based platform, it captures images of non-compliant individuals and triggers alerts. To evaluate model performance, the modified CNN is compared with the Bag of Visual Words (BoVW) method using SVM and MLP classifiers on two datasets: the 12k-Face Mask Dataset (Kaggle) and a newly proposed dataset. The CNN model demonstrated higher classification performance than both BoVW-SVM and BoVW-MLP, with AUC improvements of 49% and 43% on the proposed and 12k-Face Mask datasets, respectively.

This study contributes to the advancement of computer vision-based public health monitoring by offering a robust, scalable, and real-time face mask detection system. The findings highlight the practical advantages of deep learning approaches over traditional feature extraction techniques, supporting the development of intelligent, automated surveillance systems and policy enforcement in high-risk environments, which will facilitate future advancements in AI-driven public safety solutions.

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