An attempt to construct the individual model of daily facial skin temperature using variational autoencoder

Masaki, Ayaka; Nagumo, Kent; Iwashita, Yuki; Oiwa, Kosuke; Nozawa, Akio

doi:10.1007/s10015-021-00699-7

Cited by 5 publications

(2 citation statements)

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“…The findings, which primarily use basic parameters such as temperature variations have been observed in anomaly detection applications using the deep generative models, namely Variational Autoencoder (VAE) [69], [70]. This model is comprised of an encoder and a decoder, which help minimize overfitting and achieve a high recall.…”

Section: ) Temperature Variation Extraction a Deep Learning Methodsmentioning

confidence: 99%

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A Review of Facial Thermography Assessment for Vital Signs Estimation

2022

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Estimated vital signs might include a variety of measurements that can be used in detecting any abnormal conditions by analyzing facial images from continuous monitoring with a thermal video camera. To overcome the limitless human visual perceptions, thermal infrared has proven to be the most effective technique for visualizing facial colour changes that could have been reflected by changes in oxygenation levels and blood volume in facial arteries. This study investigated the possibility of vital signs estimation using physiological function images converted from the thermal infrared images in the same ways that visible images are used, with a need for an efficient extractor method as correction procedures that have used datasets that include images with and without wearing glasses or protective face masks. This paper, summarize thermal images using advanced machine learning and deep learning methods with satisfactory performance. Also, we presented the evaluation matrices that were included in the assessment based on statistical analysis, accuracy measures and error measures. Finally, to discuss future gaps and directions for further evaluations.INDEX TERMS Thermal images, features extractions, vital signs estimation, evaluation matrices.

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Section: ) Temperature Variation Extraction a Deep Learning Methodsmentioning

confidence: 99%

“…Normal samples were chosen as the primary indicators for analyzing the abnormality temperature patterns. In contrast in reference [70], the Hotelling theory is also utilized to assess the performance of anomaly detection.…”

Section: ) Temperature Variation Extraction a Deep Learning Methodsmentioning

confidence: 99%

A Review of Facial Thermography Assessment for Vital Signs Estimation

2022

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An attempt to construct the individual model of daily facial skin temperature using variational autoencoder

Masaki

Nagumo

Iwashita

et al. 2021

Artif Life Robotics

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Facial skin temperature (FST) has also gained prominence as an indicator for detecting anomalies such as fever due to the COVID-19. When FST is used for engineering applications, it is enough to be able to recognize normal. We are also focusing on research to detect some anomaly in FST. In a previous study, it was confirmed that abnormal and normal conditions could be separated based on FST by using a variational autoencoder (VAE), a deep generative model. However, the simulations so far have been a far cry from reality. In this study, normal FST with a diurnal variation component was defined as a normal state, and a model of normal FST in daily life was individually reconstructed using VAE. Using the constructed model, the anomaly detection performance was evaluated by applying the Hotelling theory. As a result, the area under the curve (AUC) value in ROC analysis was confirmed to be 0.89 to 1.00 in two subjects.

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