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All Journal Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT)
Muharratul Mina Rizky
Electrical and Computer Eng. Dept Universitas Syiah Kuala Banda Aceh, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Enhancing Face Detection Performance in Low-Light Conditions Using NIR Thermal Imaging and Image Morphology Maulisa Oktiana; Cut Salsabilla Azra; Rusdha Muharar; Fajrul Islamy; Rizka Ramadhana; Melinda Melinda; Niza Aulia; Muharratul Mina Rizky; Maya Fitria
Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 2 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.7.2.108786

Abstract

Face detection plays a vital role in biometric, security, and surveillance systems. Conventional approaches based on the visible light (VIS) spectrum often suffer performance degradation under poor lighting conditions, limiting their reliability. To address this issue, this study employs thermal imagery in the Near-Infrared (NIR) spectrum, which is less affected by ambient light, combined with image morphology operations to enhance segmentation accuracy. Experiments were conducted using the LDHF-DB dataset (300 images at distances of 1 m, 60 m, and 100 m) and a subset of the Tuft dataset (60 images). Face detection was performed using the HOG + SVM method, followed by Otsu thresholding and morphological operations. Performance was evaluated using Peak Signal-to-Noise Ratio (PSNR). Results show that applying morphological operations significantly improves PSNR values, with an average increase of more than 35%. The best performance was achieved on the 1 m subset, while longer distances presented greater challenges. These findings highlight the potential of integrating NIR thermal imagery and morphological processing to improve the robustness and reliability of face detection systems in low-light environments.
Co-Authors Cut Salsabilla Azra Fajrul Islamy Maulisa Oktiana Maya Fitria Melinda Melinda Niza Aulia Rizka Ramadhana Rusdha Muharar