Mobile-platform for automatic fever screening system based on infrared forehead temperature

Somboonkaew, Armote; Prempree, Panintorn; Vuttivong, Sirajit; Wetcharungsri, Jutaphet; Porntheeraphat, Supanit; Chanhorm, Sataporn; Pongsoon, Prasit; Amarit, Ratthasart; Intaravanne, Yuttana; Chaitavon, Kosom; Sumriddetchkajorn, Sarun

doi:10.1109/oecc.2017.8114910

Cited by 40 publications

(17 citation statements)

References 2 publications

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“…Lahiri [57], including fever screening. Somboonkaew et al [109] introduced a mobile platform that can be used for an automatic fever screening system using forehead temperature. Ghassemi et al [110] has discussed the best practices for standardized performance and testing of infrared thermographs.…”

Section: Representative Work Evaluation and Discussionmentioning

confidence: 99%

COVID-19 Control by Computer Vision Approaches: A Survey

et al. 2020

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The COVID-19 pandemic has triggered an urgent call to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of artificial intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at test to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources, and an indication of future research directions. We want to make it possible for computer vision researchers to find existing and future research directions. This survey paper presents a preliminary review of the literature on research community efforts against COVID-19 pandemic.

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Section: Representative Work Evaluation and Discussionmentioning

confidence: 99%

COVID-19 Control by Computer Vision Approaches: A Survey

et al. 2020

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“…It is interesting to remark that thermal cameras acquire thermal radiation emission. In this case, the emissivity of the object must be known and in the unusual case that the background provides the same thermal radiation emission as the skin of a person, thermal cameras are not enough for the extraction of the forehead region and it must be accompanied by other types of sensors as RGB [15]. Although this could solve this limitation, it will have an impact on the performance since the invested time to compute RGB images could be higher than a single 8-bit channel image as is the case of thermal images.…”

Section: Discussionmentioning

confidence: 99%

“…Fever is a concept usually linked to the forehead region and this relation is described in the work of Somboonkaew et al [15]. In their work, an automatic method to extract the highest temperature of the face and the forehead region using mobile devices is proposed.…”

Section: Related Workmentioning

confidence: 99%

Non-Invasive Forehead Segmentation in Thermographic Imaging

Rodríguez-Lozano

León‐García

Adana

et al. 2019

Sensors

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The temperature of the forehead is known to be highly correlated with the internal body temperature. This area is widely used in thermal comfort systems, lie-detection systems, etc. However, there is a lack of tools to achieve the segmentation of the forehead using thermographic images and non-intrusive methods. In fact, this is usually segmented manually. This work proposes a simple and novel method to segment the forehead region and to extract the average temperature from this area solving this lack of non-user interaction tools. Our method is invariant to the position of the face, and other different morphologies even with the presence of external objects. The results provide an accuracy of 90% compared to the manual segmentation using the coefficient of Jaccard as a metric of similitude. Moreover, due to the simplicity of the proposed method, it can work with real-time constraints at 83 frames per second in embedded systems with low computational resources. Finally, a new dataset of thermal face images is presented, which includes some features which are difficult to find in other sets, such as glasses, beards, moustaches, breathing masks, and different neck rotations and flexions.

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“…In order to limit the spread of the disease in a mass scale, the vision-based modalities have shown the most potential. CCTV footage of the public spaces, e.g., offices, markets, streets can be analyzed for monitoring socialdistancing [107], detecting face-masks [108,109], detecting high body temperature [110,111], etc. In very short period of time, some of the researches have been converted into commercial products.…”

Section: Infection Prevention and Controlmentioning

confidence: 99%

Contactless Human Activity Analysis: An Overview of Different Modalities

Abir

Faisal

Shahid

et al. 2021

Contactless Human Activity Analysis

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Human Activity Analysis (HAA) is a prominent research field in this modern era which has enlightened us with the opportunities of monitoring regular activities or the surrounding environment as per our desire. In recent times, Contactless Human Activity Analysis (CHAA) has added a new dimension in this domain as these systems perform without any wearable device or any kind of physical contact with the user. We have analyzed different modalities of CHAA and arranged them into three major categories: RF-based, sound-based, and vision-based modalities. In this chapter, we have presented state-of-the-art modalities, frequently faced challenges with some probable solutions, and currently used applications of CHAA with future directions.

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Mobile-platform for automatic fever screening system based on infrared forehead temperature

Cited by 40 publications

References 2 publications

COVID-19 Control by Computer Vision Approaches: A Survey

COVID-19 Control by Computer Vision Approaches: A Survey

Non-Invasive Forehead Segmentation in Thermographic Imaging

Contactless Human Activity Analysis: An Overview of Different Modalities

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