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A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in Various Environment Conditions and Distances From The Camera
Computer Vision and Pattern Recognition · Submitted 29 Apr 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Finally, we present a preliminary analysis of the dataset to show the applicability and importance of the thermal conditions in facial thermography.
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The abstract, sentence by sentence
Facial thermography is one of the most popular research areas in infrared thermal imaging, with diverse applications in medical, surveillance, and environmental monitoring. However, in contrast to facial imagery in the visual spectrum, the lack of public datasets on facial thermal images is an obstacle to research improvement in this area. Thermal face imagery is still a relatively new research area to be evaluated and studied in different domains.The current thermal face datasets are limited in regards to the subjects' distance from the camera, the ambient temperature variation, and facial landmarks' localization. We address these gaps by presenting a new facial thermography dataset. This article makes two main contributions to the body of knowledge. First, it presents a comprehensive review and comparison of current public datasets in facial thermography. Second, it introduces and studies a novel public dataset on facial thermography, which we call it Charlotte-ThermalFace. Charlotte-ThermalFace contains more than10000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image. Our dataset is the first publicly available thermal dataset annotated with the thermal sensation of each subject in different thermal conditions and one of the few datasets in raw 16-bit format. Finally, we present a preliminary analysis of the dataset to show the applicability and importance of the thermal conditions in facial thermography. The full dataset, including annotations, are freely available for research purpose at https://github.com/TeCSAR-UNCC/UNCC-ThermalFace
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Facial thermography is one of the most popular research areas in infrared thermal imaging, with diverse applications in medical, surveillance, and environmental monitoring. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | However, in contrast to facial imagery in the visual spectrum, the lack of public datasets on facial thermal images is an obstacle to research improvement in this area. | gap 0.97 | 0.06 | 0.03 | asserted 1.00 |
| s2 | Thermal face imagery is still a relatively new research area to be evaluated and studied in different domains.The current thermal face datasets are limited in regards to the subjects' distance from the camera, the ambient temperature variation, and facial landmarks' localization. | gap 0.96 | 0.06 | 0.03 | asserted 0.99 |
| s3 | We address these gaps by presenting a new facial thermography dataset. | contribution 1.00 | 0.86 | 0.07 | asserted 0.99 |
| s4 | This article makes two main contributions to the body of knowledge. | contribution 1.00 | 0.68 | 0.04 | asserted 0.99 |
| s5 | First, it presents a comprehensive review and comparison of current public datasets in facial thermography. | contribution 1.00 | 0.77 | 0.05 | asserted 0.99 |
| s6 | Second, it introduces and studies a novel public dataset on facial thermography, which we call it Charlotte-ThermalFace. | contribution 1.00 | 0.92 | 0.13 | asserted 1.00 |
| s7 | Charlotte-ThermalFace contains more than10000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. | contribution 0.89 | 0.90 | 0.08 | asserted 1.00 |
| s8 | The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image. | contribution 0.74 | 0.91 | 0.07 | asserted 1.00 |
| s9 | Our dataset is the first publicly available thermal dataset annotated with the thermal sensation of each subject in different thermal conditions and one of the few datasets in raw 16-bit format. | contribution 0.99 | 0.84 | 0.13 | asserted 1.00 |
| s10 | Finally, we present a preliminary analysis of the dataset to show the applicability and importance of the thermal conditions in facial thermography. | contribution 0.97 | 0.93 | 0.21 | asserted 0.96 |
| s11 | The full dataset, including annotations, are freely available for research purpose at https://github.com/TeCSAR-UNCC/UNCC-ThermalFace | contribution 0.88 | 0.66 | 0.08 | asserted 1.00 |
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Original abstract
Facial thermography is one of the most popular research areas in infrared thermal imaging, with diverse applications in medical, surveillance, and environmental monitoring. However, in contrast to facial imagery in the visual spectrum, the lack of public datasets on facial thermal images is an obstacle to research improvement in this area. Thermal face imagery is still a relatively new research area to be evaluated and studied in different domains.The current thermal face datasets are limited in regards to the subjects' distance from the camera, the ambient temperature variation, and facial landmarks' localization. We address these gaps by presenting a new facial thermography dataset. This article makes two main contributions to the body of knowledge. First, it presents a comprehensive review and comparison of current public datasets in facial thermography. Second, it introduces and studies a novel public dataset on facial thermography, which we call it Charlotte-ThermalFace. Charlotte-ThermalFace contains more than10000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image. Our dataset is the first publicly available thermal dataset annotated with the thermal sensation of each subject in different thermal conditions and one of the few datasets in raw 16-bit format. Finally, we present a preliminary analysis of the dataset to show the applicability and importance of the thermal conditions in facial thermography. The full dataset, including annotations, are freely available for research purpose at https://github.com/TeCSAR-UNCC/UNCC-ThermalFace
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