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Thematische Bibliographien / Thermal face images

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Zeitschriftenartikel

Auswahl der wissenschaftlichen Literatur zum Thema „Thermal face images“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 1. Februar 2022

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Zeitschriftenartikel zum Thema "Thermal face images"

1

Mostafa, Eslam, Riad Hammoud, Asem Ali, and Aly Farag. "Face recognition in low resolution thermal images." Computer Vision and Image Understanding 117, no. 12 (2013): 1689–94. http://dx.doi.org/10.1016/j.cviu.2013.07.010.

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2

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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3

Quan, Yan Ming, Hao Xu, and Zhi Yong Ke. "Temperature Field Measurement of Turning Tool with Thermal Infrared Imager." Advanced Materials Research 305 (July 2011): 265–68. http://dx.doi.org/10.4028/www.scientific.net/amr.305.265.

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The variety of surface emissivity of carbide insert P10 against temperature raise is calibrated with antitheses. The influence of high temperature object near the carbide insert and the open measurement environment of the thermal infrared imager on the calibrated values are investigated. Then the thermal images of turning tool’s rake face are continuously captured by an infrared imager in the designed turning experiment. In the analysis of thermal images, the influence of measurement environment is taken into consideration and varied emissivity values are used to analyze the temperature in dif

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4

Kanmani, Madheswari, and Venkateswaran Narasimhan. "Optimal fusion aided face recognition from visible and thermal face images." Multimedia Tools and Applications 79, no. 25-26 (2020): 17859–83. http://dx.doi.org/10.1007/s11042-020-08628-9.

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5

Nagumo, Kent, Tomohiro Kobayashi, Kosuke Oiwa, and Akio Nozawa. "Face Alignment in Thermal Infrared Images Using Cascaded Shape Regression." International Journal of Environmental Research and Public Health 18, no. 4 (2021): 1776. http://dx.doi.org/10.3390/ijerph18041776.

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The evaluation of physiological and psychological states using thermal infrared images is based on the skin temperature of specific regions of interest, such as the nose, mouth, and cheeks. To extract the skin temperature of the region of interest, face alignment in thermal infrared images is necessary. To date, the Active Appearance Model (AAM) has been used for face alignment in thermal infrared images. However, computation using this method is costly, and it has a low real-time performance. Conversely, face alignment of visible images using Cascaded Shape Regression (CSR) has been reported

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6

Chame, Kanchan P. "Face Recognition using Sketch, Thermal and Infrared Images." International Journal for Research in Applied Science and Engineering Technology 9, no. 1 (2021): 178–90. http://dx.doi.org/10.22214/ijraset.2021.32751.

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7

Grudzień, Artur, Marcin Kowalski, and Norbert Pałka. "Thermal Face Verification through Identification." Sensors 21, no. 9 (2021): 3301. http://dx.doi.org/10.3390/s21093301.

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This paper reports on a new approach to face verification in long-wavelength infrared radiation. Two face images were combined into one double image, which was then used as an input for a classification based on neural networks. For testing, we exploited two external and one homemade thermal face databases acquired in various variants. The method is reported to achieve a true acceptance rate of about 83%. We proved that the proposed method outperforms other studied baseline methods by about 20 percentage points. We also analyzed the issue of extending the performance of algorithms. We believe

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8

Hermosilla, Gabriel, José Luis Verdugo, Gonzalo Farias, Esteban Vera, Francisco Pizarro, and Margarita Machuca. "Face Recognition and Drunk Classification Using Infrared Face Images." Journal of Sensors 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/5813514.

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The aim of this study is to propose a system that is capable of recognising the identity of a person, indicating whether the person is drunk using only information extracted from thermal face images. The proposed system is divided into two stages, face recognition and classification. In the face recognition stage, test images are recognised using robust face recognition algorithms: Weber local descriptor (WLD) and local binary pattern (LBP). The classification stage uses Fisher linear discriminant to reduce the dimensionality of the features, and those features are classified using a classifie

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9

Sancen-Plaza, Agustin, Luis M. Contreras-Medina, Alejandro Israel Barranco-Gutiérrez, Carlos Villaseñor-Mora, Juan J. Martínez-Nolasco, and José A. Padilla-Medina. "Facial Recognition for Drunk People Using Thermal Imaging." Mathematical Problems in Engineering 2020 (April 14, 2020): 1–9. http://dx.doi.org/10.1155/2020/1024173.

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Face recognition using thermal imaging has the main advantage of being less affected by lighting conditions compared to images in the visible spectrum. However, there are factors such as the process of human thermoregulation that cause variations in the surface temperature of the face. These variations cause recognition systems to lose effectiveness. In particular, alcohol intake causes changes in the surface temperature of the face. It is of high relevance to identify not only if a person is drunk but also their identity. In this paper, we present a technique for face recognition based on the

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10

Fitriyah, Hurriyatul, Edita Rosana Widasari, and Rekyan Regasari Mardi Putri. "Inner-Canthus Localization of Thermal Images in Face-View Invariant." International Journal on Advanced Science, Engineering and Information Technology 8, no. 6 (2018): 2570. http://dx.doi.org/10.18517/ijaseit.8.5.3903.

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