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Journal : Jurnal Teknik Informatika (JUTIF)

Comparative Analysis of Face Mask Detection using Lightweight CNN and Bag of Visual Word-based Classifier for Real-Time Surveillance Candradewi, Ika; Aldino Ardi S, Bakhtiar; Harjoko, Agus; Dharmawan, Andi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4461

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.
Co-Authors Achmad Nizar Hidayanto Agus Wahyu Widodo, Agus Wahyu Ahmad Ashari Ajitomo, Wahyu Alabid, Noralhuda N. Aldino Ardi S, Bakhtiar Anak Agung Istri Ngurah Eka Karyawati Andi Dharmawan Andi Sunyoto Andiko Putro Suryotomo Aniati Murni Arymurthy Anny Kartika Sari Ashar Punto Nurwendo Azhari, Azhari Bakhtiar Alldino Ardi Sumbodo Bernard Renaldy Suteja Budi Rahardjo Budi Rahmani Dyah Aruming Tyas Edy Winarno Elizabeth Nurmiyati Tamatjita Enny Itje Sela Feri Wibowo Gamma Kosala Hadi Santoso Helna Wardhana Hermawan Syahputra I Gede Aris Gunadi I Putu Adi Pratama Ika Arfiani Ika Candradewi Ika Candradewi, Ika Ika Sudirahayu Ikhwan Ruslianto Iwan Budi Nugroho Jumanto Jumanto, Jumanto Khabib Mustofa Kusrini Kusrini La Ode Hasnuddin S. Sagala Latifah, Husnul Lilik Sutiarso Lukman Awaludin Mahmuddin Yunus Maimunah Maimunah Maura Widyaningsih Much Aziz Muslim Muhammad Anis Al Hilmi Muhammad Shahid Ardi Munakhir Mudjosemedi Murinto Murinto Mursid Wahyu Hananto Nafiiyah, Nur Nicodemus Mardanus Setiohardjo Nora Idiawati Norman Yazid Novrido Charibaldi Nugraha, Faizal Widya Pradana, Gregorius Adi Pujiastuti, Asih Raden Sumiharto Rahmad Hidayat Rahmi Hidayati Retantyo Wardoyo Rifqi Firdaus Al Jauhari Rocky Yefrenes Dillak Rudiati Evi Masithoh Sajid, Syahmi Salman Aliaji Septia Rani Setyo Nugroho Slamet Santosa Slamet Santoso Sri Hartati Sri Hartati Sri Hartati Sri Kusumadewi Sri Suwarno Tia Widiana Tri Kuntoro Priyambodo Tri Wahyu Supardi Wawan Kurniawan Winarko, Edi Winarno Winarno Yustina Retno Wahyu Utami Zaenal Abidin Zulkarnaen, M. Ari