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Dynamic Group Difference Coding Based on Thermal Infrared Face Image for Fever Screening

delete2023-01-01
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PRE
AI
M
Mengkai Yan
J
Jianjun Qian
R
Renke Wang
S
Shangbing Gao
Jian Yang 封面图
Jian Yang (Jian Yang) *
DOI:10.1109/TIM.2023.3235442delete
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摘要

摘要

En 中文
Recently, noncontact temperature measurement methods based on infrared face perception have received widely attentions since fever screening plays an important role in the early prediction of respiratory infections, such as SARS, H1N1, and COVID-19. However, the performance of these methods always significantly degrades when facing the changes of environment. Thus, the majority of these methods leverage the block-body and sensors to reduce the influence of environment changes. It is a pity that the increased instrument complexity leads to higher costs and failure rate. To address the aforementioned issues, this article presents a novel fever screening method, named dynamic group difference coding (DGDC), which is based on the analysis about the influencing factors. The key idea of DGDC is to compute the temperature differences between the target person and the recently passed crowd (dynamic group). Specifically, we develop the face temperature encoder (FTE) to describe the face temperature and thus construct the difference matrix of the embedding feature between the target person and the dynamic group. Multilayer perceptions (MLP) are employed to capture the intrinsic information by characterizing the difference matrix in vertical and horizontal directions, respectively. Finally, we provide a dataset of thermal infrared face (TIF) images and conduct extensive experiments to demonstrate the advantages of the proposed method over the competing methods.
Keyword:
Temperature measurement
Faces
Cameras
Temperature
Temperature sensors
Face recognition
Neural networks
Body temperature
COVID-19
fever screening
infrared face image
neural network

期刊

IEEE Transactions on Instrumentation and Measurement 封面图
IEEE Transactions on Instrumentation and Measurement
IF:
5.9
论文数:
2.0W
被引数:
5.8W

机构

H
Huaiyin Institute of Technology
学者数:
3.0K
论文数: 2.0K
被引数: 3.1K
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