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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Administrasi Kesehatan Indonesia Gadjah Mada Journal of Psychology JURNAL PENDIDIKAN TAMBUSAI Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) Jurnal Psikologi Universitas Gadjah Mada Buletin Psikologi Jurnal Pengabdian Masyarakat Ilmu Kesehatan Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Maya Fitria
Program Studi Psikologi Universitas Islam Negeri Sunan Kalijaga Yogyakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences

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Enhancing Face Detection Performance in Low-Light Conditions Using NIR Thermal Imaging and Image Morphology Oktiana, Maulisa; Azra, Cut Salsabilla; Muharar, Rusdha; Islamy, Fajrul; Ramadhana, Rizka; Melinda, Melinda; Aulia, Niza; Rizky, Muharratul Mina; Fitria, Maya
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 Agung Dwi Laksono Al Bahri Alruwaili, Abdulah Saleh Arrahman, Ridha Asfriyati ,MKes Aulia Ishak Aulia, Niza Avin Fadilla Helmi Azra, Cut Salsabilla Dina Bisara Lolong Erna Mutiara Halinda Sari Lubis Hasibuan, Rahmah Annisa Islamy, Fajrul Isma, Teuku Reza Auliandra Melinda Melinda Muchtar, Kahlil Oktiana, Maulisa Ramadhana, Rizka Rizky, Muharratul Mina Rohmah, Nikmatur Rusdha Muharar, Rusdha Saddami, Khairun Sayed Muchallil Siti Rohmah Situmorang, Syafrizal Helmi Sri Rahayu Sanusi Syahri, Isyatun Mardhiyah Yassar, Muhammad Keysha Al Yufnanda, Muhammad Aditya Zulfikar