scholarly journals Anomaly detection in facial skin temperature using variational autoencoder

2020 ◽  
Author(s):  
Ayaka Masaki ◽  
Kent Nagumo ◽  
Bikash Lamsal ◽  
Kosuke Oiwa ◽  
Akio Nozawa
Keyword(s):  
Anomaly Detection ◽  
Skin Temperature ◽  
Facial Skin ◽  
Autonomic Nervous System Activity ◽  
New Approach ◽  
Thermal Images ◽  
Psychological States ◽  
Detection Model ◽  
Nervous System Activity ◽  
Variational Autoencoder

Abstract Facial skin temperature is a physiological index that varies with skin blood flow controlled by autonomic nervous system activity. The facial skin temperature can be remotely measured using infrared thermography, and it has recently attracted attention as a remote biomarker. For example, studies have been reported to estimate human emotions, drowsiness, and mental stress on facial skin temperature. However, it is impossible to make a machine that can discriminate all infinite physiological and psychological states. Considering the practicality of skin temperature, a machine that can determine the normal state of facial skin temperature may be sufficient. In this study, we propose a completely new approach to incorporate the concept of anomaly detection into the analysis of physiological and psychological states by facial skin temperature. In this paper, the method for separating normal and anomaly facial thermal images using an anomaly detection model was investigated to evaluate the applicability of variational autoencoder (VAE) to facial thermal images.

scholarly journals Spatial normalization of facial thermal images using facial landmarks

2021 ◽  
Author(s):  
Kent Nagumo ◽  
Kosuke Oiwa ◽  
Akio Nozawa
Keyword(s):  
Individual Differences ◽  
Skin Temperature ◽  
Correlation Coefficient ◽  
Human Condition ◽  
Psychological State ◽  
Facial Skin ◽  
Spatial Normalization ◽  
Thermal Images ◽  
Psychological States ◽  
Face Shape

AbstractHuman–computer interaction (HCI) is an interaction for mutual communication between humans and computers. HCI needs to recognize the human state quantitatively and in real-time. Although it is possible to quantitatively evaluate the human condition by measuring biological signals, the challenge is that it often requires physical constraints. There is an increasing interest in a non-contact method of estimating physiological and psychological states by measuring facial skin temperature using infrared thermography. However, due to individual differences in face shape, the accuracy of physiological and psychological state estimation using facial thermal images was sometimes low. To solve this problem, we hypothesized that spatial normalization of facial thermal image (SN-FTI) could reduce the effect of individual differences in facial shape. The objective of this study is to develop a method for SN-FTI and to evaluate the effect of SN-FTI on the estimation of physiological and psychological states. First, we attempted spatial normalization using facial features. The results suggested that SN-FTI would result in the same face shape among individuals. Since there are individual differences in facial skin temperature distribution, the inter-individual correlation coefficient is suggested to be lower than the intra-individual correlation coefficient. Next, we modeled the estimated drowsiness level using SN-FTIs and compared it with Normal. The results showed that SN-FTI slightly improved the discrimination rate of drowsiness level. SN-FTIs were suggested to reduce the effect of individual differences in facial structure on the estimation of physiological and psychological states.

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

2021 ◽  
Author(s):  
Ayaka Masaki ◽  
Kent Nagumo ◽  
Yuki Iwashita ◽  
Kosuke Oiwa ◽  
Akio Nozawa
Keyword(s):  
Skin Temperature ◽  
Normal State ◽  
Roc Analysis ◽  
Daily Life ◽  
Area Under The Curve ◽  
Facial Skin ◽  
Individual Model ◽  
Variational Autoencoder ◽  
Auc Value ◽  
The Individual

AbstractFacial 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.

Deep learning techniques for automated detection of autism spectrum disorder based on thermal imaging

2021 ◽  
pp. 095441192110247
Author(s):  
Kavya Ganesh ◽  
Snekhalatha Umapathy ◽  
Palani Thanaraj Krishnan
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Skin Temperature ◽  
Thermal Imaging ◽  
Imaging Modality ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Facial Skin ◽  
Autistic Children ◽  
Thermal Images

Children with autism spectrum disorder have impairments in emotional processing which leads to the inability in recognizing facial expressions. Since emotion is a vital criterion for having fine socialisation, it is incredibly important for the autistic children to recognise emotions. In our study, we have chosen the facial skin temperature as a biomarker to measure emotions. To assess the facial skin temperature, the thermal imaging modality has been used in this study, since it has been recognised as a promising technique to evaluate emotional responses. The aim of this study was the following: (1) to compare the facial skin temperature of autistic and non-autistic children by using thermal imaging across various emotions; (2) to classify the thermal images obtained from the study using the customised convolutional neural network compared with the ResNet 50 network. Fifty autistic and fifty non-autistic participants were included for the study. Thermal imaging was used to obtain the temperature of specific facial regions such as the eyes, cheek, forehead and nose while we evoked emotions (Happiness, anger and sadness) in children using an audio-visual stimulus. Among the emotions considered, the emotion anger had the highest temperature difference between the autistic and non-autistic participants in the region’s eyes (1.9%), cheek (2.38%) and nose (12.6%). The accuracy obtained by classifying the thermal images of the autistic and non-autistic children using Customised Neural Network and ResNet 50 Network was 96% and 90% respectively. This computer aided diagnostic tool can be a predictable and a steadfast method in the diagnosis of the autistic individuals.

Stress Presumption of the Long Driving Using the Facial Thermal Image

2010 ◽  
Vol 22 (6) ◽  
pp. 751-757 ◽  
Author(s):  
Hirotoshi Asano ◽  
◽  
Hitoshi Onogaki ◽  
Takumi Muto ◽  
Syuichi Yokoyama ◽  
...  
Keyword(s):  
Skin Temperature ◽  
Stress Level ◽  
Mental State ◽  
Thermal Image ◽  
Temperature Pattern ◽  
Facial Skin ◽  
Psychological States ◽  
Quantitative Measurements ◽  
Car Accidents ◽  
Close Relationship

There is a close relationship between car accidents and the physiological and psychological states of drivers. Stress may lead to a feeling of fatigue or a decrease in attentiveness. Therefore, it is an important subject from viewpoints such as that of accident prevention to evaluate the mental state of drivers behind the wheel. This research aims at the development of technology that will take quantitative measurements of stress based on facial skin temperature. It is based on the relation between facial skin temperature and changes in mental state. Presumption of stress level of a driver was attempted from the change in temperature pattern of a series of readings.

Measuring Facial Skin Temperature Changes Caused by Mental Work-Load with Infrared Thermography

2016 ◽  
Vol 136 (11) ◽  
pp. 1581-1585 ◽  
Author(s):  
Tota Mizuno ◽  
Takeru Sakai ◽  
Shunsuke Kawazura ◽  
Hirotoshi Asano ◽  
Kota Akehi ◽  
...  
Keyword(s):  
Skin Temperature ◽  
Infrared Thermography ◽  
Work Load ◽  
Facial Skin ◽  
Mental Work ◽  
Temperature Changes ◽  
Mental Work Load

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

Toward Unsupervised 3d Point Cloud Anomaly Detection Using Variational Autoencoder

2021 ◽  
Author(s):  
Mana Masuda ◽  
Ryo Hachiuma ◽  
Ryo Fujii ◽  
Hideo Saito ◽  
Yusuke Sekikawa
Keyword(s):  
Anomaly Detection ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Variational Autoencoder
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