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Journal articles on the topic 'Face detection on thermal image'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Seo, Jongwoo, and In-Jeong Chung. "Face Liveness Detection Using Thermal Face-CNN with External Knowledge." Symmetry 11, no. 3 (2019): 360. http://dx.doi.org/10.3390/sym11030360.

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Face liveness detection is important for ensuring security. However, because faces are shown in photographs or on a display, it is difficult to detect the real face using the features of the face shape. In this paper, we propose a thermal face-convolutional neural network (Thermal Face-CNN) that knows the external knowledge regarding the fact that the real face temperature of the real person is 36~37 degrees on average. First, we compared the red, green, and blue (RGB) image with the thermal image to identify the data suitable for face liveness detection using a multi-layer neural network (MLP

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Hiroki, Ito Kosuke Oiwa and Akio Nozawa: Aoyama Gakuin University Japan. "Image Segmentation-Based Face Tracking on Thermal Images for Automatic Estimation of Psychophysiological States Using Facial Skin Temperature Distribution." Journal of Information Bioinformatics and Neuroscience (JBINS) Volume 4, Issue 1 (2020): 142–46. https://doi.org/10.5281/zenodo.4273823.

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Abstract: In human-machine system, human and machine need to recognize each other’s state with continuously, quantitatively and real-time property. Facial skin temperature could be measured with these properties by infrared thermography. The non-contact property is a great advantage in bioinstrumentation. Previous studies have been reported the availability of facial skin temperature for evaluation of psychophysiological states of a human such as stress, drowsiness and emotion. On the other hand, the development of the face detection and tracking techniques on thermal images are necessar

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Albar, Albar, Hendrick Hendrick, and Rahmad Hidayat. "Segmentation Method for Face Modelling in Thermal Images." Knowledge Engineering and Data Science 3, no. 2 (2020): 99. http://dx.doi.org/10.17977/um018v3i22020p99-105.

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Face detection is mostly applied in RGB images. The object detection usually applied the Deep Learning method for model creation. One method face spoofing is by using a thermal camera. The famous object detection methods are Yolo, Fast RCNN, Faster RCNN, SSD, and Mask RCNN. We proposed a segmentation Mask RCNN method to create a face model from thermal images. This model was able to locate the face area in images. The dataset was established using 1600 images. The images were created from direct capturing and collecting from the online dataset. The Mask RCNN was configured to train with 5 epoc

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Pavez, Vicente, Gabriel Hermosilla, Manuel Silva, and Gonzalo Farias. "Advanced Deep Learning Techniques for High-Quality Synthetic Thermal Image Generation." Mathematics 11, no. 21 (2023): 4446. http://dx.doi.org/10.3390/math11214446.

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In this paper, we introduce a cutting-edge system that leverages state-of-the-art deep learning methodologies to generate high-quality synthetic thermal face images. Our unique approach integrates a thermally fine-tuned Stable Diffusion Model with a Vision Transformer (ViT) classifier, augmented by a Prompt Designer and Prompt Database for precise image generation control. Through rigorous testing across various scenarios, the system demonstrates its capability in producing accurate and superior-quality thermal images. A key contribution of our work is the development of a synthetic thermal fa

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Ma, Chao, Ngo Trung, Hideaki Uchiyama, Hajime Nagahara, Atsushi Shimada, and Rin-ichiro Taniguchi. "Adapting Local Features for Face Detection in Thermal Image." Sensors 17, no. 12 (2017): 2741. http://dx.doi.org/10.3390/s17122741.

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Cho, Se, Na Baek, Min Kim, Ja Koo, Jong Kim, and Kang Park. "Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network." Sensors 18, no. 9 (2018): 2995. http://dx.doi.org/10.3390/s18092995.

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Conventional nighttime face detection studies mostly use near-infrared (NIR) light cameras or thermal cameras, which are robust to environmental illumination variation and low illumination. However, for the NIR camera, it is difficult to adjust the intensity and angle of the additional NIR illuminator according to its distance from an object. As for the thermal camera, it is expensive to use as a surveillance camera. For these reasons, we propose a nighttime face detection method based on deep learning using a single visible-light camera. In a long-distance night image, it is difficult to dete

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Li, Shuoda, Xinyao Wang, and Xicai Li. "Application of Visual Transformer in Low-resolution Thermal Infrared Image Recognition." Journal of Physics: Conference Series 2868, no. 1 (2024): 012031. http://dx.doi.org/10.1088/1742-6596/2868/1/012031.

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Abstract Addressing the challenges of inadequate accuracy and limited robustness exhibited by current lightweight object detection networks specifically tailored for low-resolution thermal infrared face detection scenarios, this paper delves into developing an ultra-lightweight thermal infrared face detection algorithm that leverages visual attention mechanisms. To ascertain the optimal neural network complexity, a series of comparative experiments are meticulously conducted. With Yolo-FastestDet serving as the benchmark, this study endeavors to compress the backbone network, striking a delica

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Kowalski, Marcin, and Krzysztof Mierzejewski. "Detection of 3D face masks with thermal infrared imaging and deep learning techniques." Photonics Letters of Poland 13, no. 2 (2021): 22. http://dx.doi.org/10.4302/plp.v13i2.1091.

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Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural ne

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Latinović, Nikola, Tijana Vuković, Ranko Petrović, et al. "Implementation challenge and analysis of thermal image degradation on R-CNN face detection." Telfor Journal 12, no. 2 (2020): 98–103. http://dx.doi.org/10.5937/telfor2002098l.

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Face detection systems with color cameras were rapidly evolving and have been well researched. In environments with good visibility they can reach excellent accuracy. But changes in illumination conditions can result in performance degradation, which is the one of the major limitations in visible light face detection systems. The solution to this problem could be in using thermal infrared cameras, since their operation doesn't depend on illumination. Recent studies have shown that deep learning methods can achieve an impressive performance on object detection tasks, and face detection in parti

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Trongtirakul, Thaweesak, Karen Panetta, Artyom M. Grigoryan, and Sos S. Agaian. "A Novel Entropy-Based Approach for Thermal Image Segmentation Using Multilevel Thresholding." Entropy 27, no. 5 (2025): 526. https://doi.org/10.3390/e27050526.

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Image segmentation is a fundamental challenge in computer vision, transforming complex image representations into meaningful, analyzable components. While entropy-based multilevel thresholding techniques, including Otsu, Shannon, fuzzy, Tsallis, Renyi, and Kapur approaches, have shown potential in image segmentation, they encounter significant limitations when processing thermal images, such as poor spatial resolution, low contrast, lack of color and texture information, and susceptibility to noise and background clutter. This paper introduces a novel adaptive unsupervised entropy algorithm (A

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Mahajan, Palak, Pawanesh Abrol, and Parveen Kumar Lehana. "Thermal imaging-based identification of facial features in noisy environment." International Journal of Informatics and Communication Technology (IJ-ICT) 13, no. 3 (2024): 333. http://dx.doi.org/10.11591/ijict.v13i3.pp333-343.

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<p class="Abstract">Face identification is amongst the most efficacious and extensive applications in biometrics involving extraction and locating facial features. With identification being monotonous task attributable to reliance on parameters like varied cameras, fluctuating backgrounds, and exposure to the environment in which an individual is present. Thermal imaging is endeavoring to resolve the accuracy issue of apparent imaging, such as lighting and brightness intensity, among all biometric variables. This paper presents a study of thermal imaging and effective methods involved in

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Palak, Mahajan, Abrol Pawanesh, and Kumar Lehana Parveen. "Thermal imaging-based identification of facial features in noisy environment." International Journal of Informatics and Communication Technology 13, no. 3 (2024): 333–43. https://doi.org/10.11591/ijict.v13i3.pp333-343.

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Face identification is amongst the most efficacious and extensive applications in biometrics involving extraction and locating facial features. With identification being monotonous task attributable to reliance on parameters like varied cameras, fluctuating backgrounds, and exposure to the environment in which an individual is present. Thermal imaging is endeavoring to resolve the accuracy issue of apparent imaging, such as lighting and brightness intensity, among all biometric variables. This paper presents a study of thermal imaging and effective methods involved in the feature extraction pr

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Quirino, Matteo, and Michèle Roberta Lavagna. "Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios." Applied Sciences 14, no. 13 (2024): 5377. http://dx.doi.org/10.3390/app14135377.

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On-orbit autonomous relative navigation performance strongly depends on both sensor suite and state reconstruction selection. Whenever that suite relies on image-based sensors working in the visible spectral band, the illumination conditions strongly affect the accuracy and robustness of the state reconstruction outputs. To cope with that limitation, we investigate the effectiveness of exploiting image sensors active in the IR spectral band, not limited by the lighting conditions. To run effective and comprehensive testing and validation campaigns on navigation algorithms, a large dataset of i

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Fitriyah, Hurriyatul, and Edita Rosana Widasari. "Face Detection of Thermal Images in Various Standing Body-Pose using Facial Geometry." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14, no. 4 (2020): 407. http://dx.doi.org/10.22146/ijccs.59672.

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Automatic face detection in frontal view for thermal images is a primary task in a health system e.g. febrile identification or security system e.g. intruder recognition. In a daily state, the scanned person does not always stay in frontal face view. This paper develops an algorithm to identify a frontal face in various standing body-pose. The algorithm used an image processing method where first it segmented face based on human skin’s temperature. Some exposed non-face body parts could also get included in the segmentation result, hence discriminant features of a face were applied. The shape

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Rakeshkumar, H. Yadav, Agarwal Brajgopal, and James Sheeba. "Face Recognition System." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 1815–18. https://doi.org/10.31142/ijtsrd14453.

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Face is a important part through which we can identify who we are and how people identify us. Face is a persons most valuable and unique physical characteristics through which we can identify someone very easily. While humans have the innate ability to distinguish the different faces for millions of years for adding the new technology computers are just now catching up. A face recognition system is a computer application that is capable of identifying or verifying the person from a digital image or a video frame from video source. One of the way is to do this is by compare with the selected fa

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Hendryani, Atika, Vita Nurdinawati, and Andy Sambiono. "Implementation of Thermal Camera for Human Stress Detection: A Review." International Journal of Electrical, Computer, and Biomedical Engineering 1, no. 2 (2023): 108–19. http://dx.doi.org/10.62146/ijecbe.v1i2.28.

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Stress has become a major problem that people face today. The high level of competition and environmental demands make people more susceptible to stress. Stress can interfere with a person's ability to work effectively. If left unchecked for a long time, stress can cause various dangerous diseases such as hypertension, heart problems, and others that can lead to death. Research has been conducted for a long time to detect stress. Various technologies have been used to detect and anticipate stress that occurs in humans. One promising technology for detecting stress is the use of thermal cameras

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Nowakowski, Antoni Z., and Mariusz Kaczmarek. "Artificial Intelligence in IR Thermal Imaging and Sensing for Medical Applications." Sensors 25, no. 3 (2025): 891. https://doi.org/10.3390/s25030891.

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The state of the art in IR thermal imaging methods for applications in medical diagnostics is discussed. A review of advances in IR thermal imaging technology in the years 1960–2024 is presented. Recently used artificial intelligence (AI) methods in the analysis of thermal images are the main interest. IR thermography is discussed in view of novel applications of machine learning methods for improved diagnostic analysis and medical treatment. The AI approach aims to improve image quality by denoising thermal images, using applications of AI super-resolution algorithms, removing artifacts, obje

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Junidar, Junidar, Melinda Melinda, Dinda D. Diannuari, Donata D. Acula, and Zulfan Zainal. "Face autistic classification based on thermal using image ensemble learning of VGG-19, ResNet50v2, and EfficientNet." Radioelectronic and Computer Systems 2025, no. 1 (2025): 153–64. https://doi.org/10.32620/reks.2025.1.11.

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The subject of this paper is the detection of Autism Spectrum Disorder (ASD) traits using facial recognition based on thermal images. The goal of this study was to evaluate and compare the performance of various Convolutional Neural Network (CNN) architectures in classifying thermal facial images of children with ASD, thereby facilitating the early identification of autistic traits. The tasks addressed include preprocessing a dataset of thermal facial images to prepare them for model training; conducting classification using three CNN architectures VGG-19, ResNet50V2, and EfficientNet; and ass

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Yao, K., Y. Chen, Z. D. Zhang, et al. "Identity and body temperature detection system based on image registration." Journal of Physics: Conference Series 2290, no. 1 (2022): 012073. http://dx.doi.org/10.1088/1742-6596/2290/1/012073.

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Abstract In view of the normalization of epidemic prevention and control work, the low detection efficiency of traditional temperature measurement methods, and the existence of infection risks, an infrared thermal imaging temperature measurement system that can measure and identify the temperature of more than two people is designed. The system uses the Raspberry Pi as the core controller, using visible light and infrared cameras to capture identity and body temperature information. Identity recognition is based on OpenCv, using the Face Recognition face recognition framework to complete the i

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Prasad Singothu, Babu Rajendra, and Bolem Sai Chandana. "Objects and Action Detection of Human Faces through Thermal Images Using ANU-Net." Sensors 22, no. 21 (2022): 8242. http://dx.doi.org/10.3390/s22218242.

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Thermal cameras, as opposed to RBG cameras, work effectively in extremely low illumination situations and can record data outside of the human visual spectrum. For surveillance and security applications, thermal images have several benefits. However, due to the little visual information in thermal images and intrinsic similarity of facial heat maps, completing face identification tasks in the thermal realm is particularly difficult. It can be difficult to attempt identification across modalities, such as when trying to identify a face in thermal images using the ground truth database for the m

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Alves, Marcos G., Gen-Lang Chen, Xi Kang, and Guang-Hui Song. "Reduced CPU Workload for Human Pose Detection with the Aid of a Low-Resolution Infrared Array Sensor on Embedded Systems." Sensors 23, no. 23 (2023): 9403. http://dx.doi.org/10.3390/s23239403.

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Modern embedded systems have achieved relatively high processing power. They can be used for edge computing and computer vision, where data are collected and processed locally, without the need for network communication for decision-making and data analysis purposes. Face detection, face recognition, and pose detection algorithms can be executed with acceptable performance on embedded systems and are used for home security and monitoring. However, popular machine learning frameworks, such as MediaPipe, require relatively high usage of CPU while running, even when idle with no subject in the sc

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van Doremalen, Rob F. M., Jaap J. van Netten, Jeff G. van Baal, Miriam M. R. Vollenbroek-Hutten, and Ferdinand van der Heijden. "Infrared 3D Thermography for Inflammation Detection in Diabetic Foot Disease: A Proof of Concept." Journal of Diabetes Science and Technology 14, no. 1 (2019): 46–54. http://dx.doi.org/10.1177/1932296819854062.

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Background: Thermal assessment of the plantar surface of the foot using spot thermometers and thermal imaging has been proven effective in diabetic foot ulcer prevention. However, with traditional cameras this is limited to single spots or a two-dimensional (2D) view of the plantar side of foot, where only 50% of the ulcers occur. To improve ulcer detection, the view has to be extended beyond 2D. Our aim is to explore for proof of concept the combination of three-dimensional (3D) models with thermal imaging for inflammation detection in diabetic foot disease. Method: From eight participants wi

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Głowacka, Natalia, and Jacek Rumiński. "Face with Mask Detection in Thermal Images Using Deep Neural Networks." Sensors 21, no. 19 (2021): 6387. http://dx.doi.org/10.3390/s21196387.

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As the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The goal of this work is to analyze the effectiveness of deep-learning-based face detection algorithms applied to thermal images, especially for faces covered by virus pr

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Babu Rajendra Prasad, S., and B. Sai Chandana. "Human Face Emotions Recognition from Thermal Images Using DenseNet." International journal of electrical and computer engineering systems 14, no. 2 (2023): 155–67. http://dx.doi.org/10.32985/ijeces.14.2.5.

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In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the perform

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Martin, Pierre-Etienne. "ApeTI: A Thermal Image Dataset for Face and Nose Segmentation with Apes." Signals 5, no. 1 (2024): 147–64. http://dx.doi.org/10.3390/signals5010008.

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The ApeTI dataset was built with the aim of retrieving physiological signals such as heart rate, breath rate, and cognitive load from thermal images of great apes. We want to develop computer vision tools that psychologists and animal behavior researchers can use to retrieve physiological signals noninvasively. Our goal is to increase the use of a thermal imaging modality in the community and avoid using more invasive recording methods to answer research questions. The first step to retrieving physiological signals from thermal imaging is their spatial segmentation to then analyze the time ser

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Guo, Shih-Sian, Kuo-Hua Lee, Liyun Chang, et al. "Development of an Automated Body Temperature Detection Platform for Face Recognition in Cattle with YOLO V3-Tiny Deep Learning and Infrared Thermal Imaging." Applied Sciences 12, no. 8 (2022): 4036. http://dx.doi.org/10.3390/app12084036.

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This study developed an automated temperature measurement and monitoring platform for dairy cattle. The platform used the YOLO V3-tiny (you only look once, YOLO) deep learning algorithm to identify and classify dairy cattle images. The system included a total of three layers of YOLO V3-tiny identification: (1) dairy cow body; (2) individual number (identity, ID); (3) thermal image of eye socket identification. We recorded each cow’s individual number and body temperature data after the three layers of identification, and carried out long-term body temperature tracking. The average prediction s

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Robledo-Vega, Isidro, Scarllet Osuna-Tostado, Abraham Efraím Rodríguez-Mata, Carmen Leticia García-Mata, Pedro Rafael Acosta-Cano, and Rogelio Enrique Baray-Arana. "Embedded Vision System for Thermal Face Detection Using Deep Learning." Sensors 25, no. 10 (2025): 3126. https://doi.org/10.3390/s25103126.

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Face detection technology is essential for surveillance and security projects; however, algorithms designed to detect faces in color images often struggle in poor lighting conditions. In this paper, we describe the development of an embedded vision system designed to detect human faces by analyzing images captured with thermal infrared sensors, thereby overcoming the limitations imposed by varying illumination conditions. All variants of the Ultralytics YOLOv8 and YOLO11 models were trained on the Terravic Facial IR database and tested on the Charlotte-ThermalFace database; the YOLO11 model ac

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DR., Y. M. PATIL. "FACE SEGMENTATION, VASCULAR FEATURE EXTRACTION AND MINUTIAE EXTRACTION FOR PERSON IDENTIFICATION USING THERMAL IMAGE." IJIERT - International Journal of Innovations in Engineering Research and Technology 3, no. 6 (2016): 81–86. https://doi.org/10.5281/zenodo.1463712.

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<strong>The paper discusses the vascular feature detection of human face using thermal images and the inter process results. Here we have designed an approach to segment the face of photographed person. Then the segmented face is processed through anisotropic filter to remove noise present in it. Then we have also discussed the methods to extract blood vessel structure from the noise removed face. The results of the segmentation and of anisotropic filter are discussed. Through application of anisotropic filter noise is removed in fairly good amount and post processing becomes easy. Discussed c

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Bedoya-Echeverry, Sebastián, Hernán Belalcázar-Ramírez, Humberto Loaiza-Correa, Sandra Esperanza Nope-Rodríguez, Carlos Rafael Pinedo-Jaramillo, and Andrés David Restrepo-Girón. "Detection of lies by facial thermal imagery analysis." Revista Facultad de Ingeniería 26, no. 44 (2017): 45. http://dx.doi.org/10.19053/01211129.v26.n44.2017.5771.

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An artificial vision system is presented for lie detection by analyzing face thermal image sequences. This system represents an alternative technique to the polygraph. Some of its features are: 1) it has no physical contact with the examinee, 2) it is non-intrusive, 3) it has a potential for private use, and 4) it can simultaneously analyze several persons. The proposed system is based on the detection of physiological changes in temperature in the lacrimal puncta area caused by the subtle increase in blood flow through the nearby vascular network. These changes take place when anxiety appears

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Türkler, Levent, Taner Akkan, and Lütfiye Özlem Akkan. "Detection of Water Leakage in Drip Irrigation Systems Using Infrared Technique in Smart Agricultural Robots." Sensors 23, no. 22 (2023): 9244. http://dx.doi.org/10.3390/s23229244.

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In the future, the world is likely to face water and therefore food shortages due to reasons such as global warming, population growth, the melting of glaciers, the destruction of agricultural lands over time or their use for different purposes, and environmental pollution. Although technological developments are important for people to live a more comfortable and safer life, it is also possible to reduce and even repair the damage to nature and protect nature itself thanks to new technologies. There is a requirement to detect abnormal water usage in agriculture to avert water scarcity, and an

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Kopaczka, Marcin, Lukas Breuer, Justus Schock, and Dorit Merhof. "A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings." Sensors 19, no. 19 (2019): 4135. http://dx.doi.org/10.3390/s19194135.

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We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark deteci

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Imam, Abdulrafiu Musa. "Biometric Presentation Attack Detection." American Journal of Computing and Engineering 8, no. 1 (2025): 32–56. https://doi.org/10.47672/ajce.2631.

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Abstract Purpose: Biometric systems play a crucial role in authentication and identification processes but are vulnerable to various attacks that compromise their security and reliability. Detecting such attacks is critical to ensuring the integrity of these systems and maintaining user trust. This study focuses on detecting face presentation attacks using a cost-effective thermal sensor array. The primary goal is to combine an RGB camera, a thermal sensor array, and deep convolutional neural networks (CNNs) to differentiate between genuine face presentations and facial presentation attacks. T

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Swaroop A, Krishna. "Human Emotion Detection Using Thermal Images." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46850.

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Abstract-In healthcare, security, and even in human-computer interactions, the application of emotion detection technology is becoming increasingly popular. Exiting methods employing light pictures have problems with occlusion and illumination. This project uses thermal imaging to identify the changes in facial temperatures due to emotions, which guarantees functionality in all lighting conditions. It also uses real-time emotion detection from thermal face data using an optimized YOLOv5 model. The model is trained using labeled thermal datasets and is modified using transfer learning to stream

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Astawa, I. Nyoman Gede Arya, I. D. G. Ary Subagia, Felipe P. Vista IV, IGAK Cathur Adhi, and I. Made Ari Dwi Suta Atmaja. "Roboswab: A Covid-19 Thermal Imaging Detector Based on Oral and Facial Temperatures." JOIV : International Journal on Informatics Visualization 7, no. 1 (2023): 221. http://dx.doi.org/10.30630/joiv.7.1.1505.

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The SARS-CoV-2 virus has been the precursor of the coronavirus disease (COVID-19). The symptoms of COVID-19 begin with the common cold and then become very severe, such as those of Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Currently, polymerase chain reaction (PCR) is used to detect COVID-19 accurately, but it causes some side effects to the patient when the test is performed. Therefore, the proposed "Roboswab" was developed that uses thermal imaging to measure non-contact facial and oral temperature. This study focuses on the performance of the prop

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Saroj Kumari. "Infrared Thermography (IRT) and CNN-Yolo based Emotion and Face Recognition." Journal of Electrical Systems 20, no. 10s (2024): 8530–36. https://doi.org/10.52783/jes.8762.

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Since convolutional neural networks have been used in computer vision, emotion identification has emerged as a real-world issue that needs to be addressed. On the other hand, determining feelings based on everyday visuals or entertainment is not a reliable enough method. In light of the fact that humans are able to readily repeat emotions one after the other, therefore tricking algorithms that have been taught, a fresh method need to be taken into account. There is a possibility that thermal cameras might be an appropriate method for the development of more accurate emotion identification algo

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Qin, Wenjin, Jinghan Wang, and Keyan Zhao. "A breathing rate detection method based on infrared thermal imaging video." Journal of Physics: Conference Series 2990, no. 1 (2025): 012004. https://doi.org/10.1088/1742-6596/2990/1/012004.

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Abstract With the continuous enrichment of material life, people’s concern for personal health is increasing, and respiratory rate, as an important indicator for assessing human health, is particularly important in medical monitoring. In this paper, we propose a method for respiratory rate analysis using thermal infrared imaging images, which implements face recognition, performs image tracking through HOG combined with VSM, and finally extracts the nose region containing only the respiration-related areas to obtain the average grey curve, which effectively reduces the noise of irrelevant pixe

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Marzec, Mariusz, Robert Koprowski, Zygmunt Wróbel, Agnieszka Kleszcz, and Sławomir Wilczyński. "Automatic method for detection of characteristic areas in thermal face images." Multimedia Tools and Applications 74, no. 12 (2013): 4351–68. http://dx.doi.org/10.1007/s11042-013-1745-9.

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Jaramillo-Quintanar, Daniel, Jean K. Gomez-Reyes, Luis A. Morales-Hernandez, Benjamin Dominguez-Trejo, David A. Rodriguez-Medina, and Irving A. Cruz-Albarran. "Automatic Segmentation of Facial Regions of Interest and Stress Detection Using Machine Learning." Sensors 24, no. 1 (2023): 152. http://dx.doi.org/10.3390/s24010152.

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Stress is a factor that affects many people today and is responsible for many of the causes of poor quality of life. For this reason, it is necessary to be able to determine whether a person is stressed or not. Therefore, it is necessary to develop tools that are non-invasive, innocuous, and easy to use. This paper describes a methodology for classifying stress in humans by automatically detecting facial regions of interest in thermal images using machine learning during a short Trier Social Stress Test. Five regions of interest, namely the nose, right cheek, left cheek, forehead, and chin, ar

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Nata Septiadi, Wayan, Ni Made Dian Sulistiowati, and Abdul Wakhid. "THERMOGRAPHIC EVALUATION FOR THE DIVERSE STAGE OF ANXIETY ON FACE TEMPERATURE AT FRONTAL AND TEMPORAL USING THERMAL IMAGING." Humanities & Social Sciences Reviews 7, no. 5 (2019): 1130–36. http://dx.doi.org/10.18510/hssr.2019.75149.

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that it is able to provide accurate results about the temperature picture. The purpose of this study was to examine if there were differences in anxiety conditions at facial temperatures measured using thermal imaging. Methodology: Eighty-one participants were taking the pre-clinical exams was chosen as the inclusion criteria and were divided into four categories of anxiety range (not anxious, mild anxiety, moderate anxiety, and severe anxiety) based on their score measured that using the General Anxiety Disorder (GAD-7) as the instrument. The participants were measured their face temper

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Her, Gumiwang Ariswati, and Soetjiatie Liliek. "Aura detection using thermal camera with convolutional neural network method for mental health diagnosis." Aura detection using thermal camera with convolutional neural network method for mental health diagnosis 31, no. 1 (2023): 553–61. https://doi.org/10.11591/ijeecs.v31.i1.pp553-561.

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Mental health is an important aspect in realizing overall health. For this reason, non-invasive medical equipment is needed for people with mental disorders. This study aimed to create a psychological health diagnostic tool by detecting auras using a thermal camera from facial objects. The contribution of this study is that the tool can detect the patient's aura without physical contact so that the patient is more comfortable and does not feel invaded. This research designed a system for detecting electromagnetic wave radiation energy emitted by the body using a thermal camera. Face detect

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Ariswati, Her Gumiwang, and Liliek Soetjiatie. "Aura detection using thermal camera with convolutional neural network method for mental health diagnosis." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 1 (2023): 553. http://dx.doi.org/10.11591/ijeecs.v31.i1.pp553-561.

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Mental health is an important aspect in realizing overall health. For this reason, non-invasive medical equipment is needed for people with mental disorders. This study aimed to create a psychological health diagnostic tool by detecting auras using a thermal camera from facial objects. The contribution of this study is that the tool can detect the patient's aura without physical contact so that the patient is more comfortable and does not feel invaded. This research designed a system for detecting electromagnetic wave radiation energy emitted by the body using a thermal camera. Face detection

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Gleichauf, Johanna, Lukas Hennemann, Fabian B. Fahlbusch, Oliver Hofmann, Christine Niebler, and Alexander Koelpin. "Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks." Sensors 23, no. 10 (2023): 4910. http://dx.doi.org/10.3390/s23104910.

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The monitoring of vital signs and increasing patient comfort are cornerstones of modern neonatal intensive care. Commonly used monitoring methods are based on skin contact which can cause irritations and discomfort in preterm neonates. Therefore, non-contact approaches are the subject of current research aiming to resolve this dichotomy. Robust neonatal face detection is essential for the reliable detection of heart rate, respiratory rate and body temperature. While solutions for adult face detection are established, the unique neonatal proportions require a tailored approach. Additionally, su

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Yuniardi, Doddi, Sarifuddin Madenda, Ridwan Ridwan, Prihandoko Prihandoko, Abdul Azis Abdillah, and Sulaksana Permana. "Design and application of CNN for emission detection through thermal imagery." Eastern-European Journal of Enterprise Technologies 6, no. 10 (132) (2024): 6–18. https://doi.org/10.15587/1729-4061.2024.317203.

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Motorcycle exhaust emissions (EE) that do not meet regulatory standards present a significant environmental and public health issue, particularly given the rising number of motorcycles in densely populated areas. These emissions release pollutants such as carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx), which contribute to poor air quality and have adverse effects on human health. Traditional emission testing methods using gas analyzers, while commonly used, face limitations such as sensitivity to environmental fluctuations, the necessity for frequent recalibration, and an i

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Abdullah, Al Nayeem Mahmud Lavu, Zhang Hua, Anisul Islam Jonayed MD, and Toufik Hossain MD. "Indoor Smoking Detection Method based on Dual Spectral Fusion Image and YOLO framework." LC International Journal of STEM 5, no. 3 (2024): 13–35. https://doi.org/10.5281/zenodo.14028770.

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Indoor fires are a major problem for public safety, with smoking being the most hidden threat. Traditional fire detection systems, such as smoke detectors, are only useful in the early stages and face challenges due to low light and limited visibility. This article describes an indoor smoking detection system that combines visible and infrared image fusion with the YOLO (You Only Look Once) detection framework. This technique improves indoor smoking detection performance by combining infrared thermal data with deep learning concepts. The YOLOv9 system detects indoor smoking behavior using a de

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Sun, Pengcheng, Dan Zeng, Xiaoyan Li, et al. "A 3D Mask Presentation Attack Detection Method Based on Polarization Medium Wave Infrared Imaging." Symmetry 12, no. 3 (2020): 376. http://dx.doi.org/10.3390/sym12030376.

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Facial recognition systems are often spoofed by presentation attack instruments (PAI), especially by the use of three-dimensional (3D) face masks. However, nonuniform illumination conditions and significant differences in facial appearance will lead to the performance degradation of existing presentation attack detection (PAD) methods. Based on conventional thermal infrared imaging, a PAD method based on the medium wave infrared (MWIR) polarization characteristics of the surface material is proposed in this paper for countering a flexible 3D silicone mask presentation attack. A polarization MW

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B S, Shruthi, and Manasa K B. "USER AUTHENTICATION USING EYE-BLINK PASSWORD." International Journal of Computer Science and Mobile Computing 12, no. 4 (2023): 42–46. http://dx.doi.org/10.47760/ijcsmc.2023.v12i04.004.

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Personal identification numbers (PINS) are used to authenticate a user for security purpose. In order to authenticate himself the user should physically input the PIN, which might be susceptible to password cracking via shoulder surfing or thermal tracking. Authenticating a PIN with hands-off eye blinks PIN entry techniques, on the opposite hand, offers a safer password entry option by not leaving any physical footprints behind. User authentication using eye-blink password refers to finding the attention blinks in sequential image frames, and generation of PIN. This paper presents an applicati

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Hong, Xiaobin, Guanqiao Chen, Yuanming Chen, and Ruimou Cai. "Research on Abnormal Ship Brightness Temperature Detection Based on Infrared Image Edge-Enhanced Segmentation Network." Applied Sciences 15, no. 7 (2025): 3551. https://doi.org/10.3390/app15073551.

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Infrared imaging is based on thermal radiation and does not rely on visible light, allowing for it to operate normally at night and in low-light conditions. This characteristic is beneficial for regulatory authorities to monitor ships. Existing infrared image segmentation methods face challenges such as the absence of color information, blurred edges, weak high-frequency details, and low contrast due to the imaging principles. Consequently, the segmentation accuracy for small-sized ship targets and edges is low, influenced by the indistinct features of infrared images and the weak difference b

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Makino Antunes, Ana Carolina, Alexandre Aldred, Gabriela Pinheiro Tirado Moreno, et al. "Potential of using facial thermal imaging in patient triage of flu-like syndrome during the COVID-19 pandemic crisis." PLOS ONE 18, no. 1 (2023): e0279930. http://dx.doi.org/10.1371/journal.pone.0279930.

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The screening of flu-like syndrome is difficult due to nonspecific symptoms or even oligosymptomatic presentation and became even more complex during the Covid-19 pandemic. However, an efficient screening tool plays an important role in the control of highly contagious diseases, allowing more efficient medical-epidemiological approaches and rational management of global health resources. Infrared thermography is a technique sensitive to small alterations in the skin temperature which may be related to early signs of inflammation and thus being relevant in the detection of infectious diseases.

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Kim, Yong Joong, Byung Sang Choi, Ki Seop Lee, and Kyung Kwon Jung. "Design of Face with Mask Detection System in Thermal Images Using Deep Learning." Jouranl of Information and Security 22, no. 2 (2022): 21–26. http://dx.doi.org/10.33778/kcsa.2022.22.2.021.

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Rao, M. Sivasankara, K. Tejasree, P. Sathwik, P. Sandeep Kumar, and M. Sailohith. "Real Time Face Mask Detection and Thermal Screening with Audio Response for COVID-19." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 2703–14. http://dx.doi.org/10.47059/revistageintec.v11i4.2311.

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The coronavirus COVID-19 pandemic is continuously spreading until now everywhere on the earth, and causing a severe health crisis. So the helpful and safe-keeping method is wearing a face mask in all areas where people are gathered, according to the World Health Organization (WHO). Along with the face mask, body temperature and sanitization also plays a vital role in being safer. Thus, monitoring the individuals that are wearing the mask or not is more significant. In this paper, we propose a system that uses TensorFlow, Keras, MobileNetV2, and OpenCV to detect the face mask. A dataset contain

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