Classifying Affective States Using Thermal Infrared Imaging of the Human Face

Nhan, Brian; Chau, Tom

doi:10.1109/tbme.2009.2035926

Cited by 127 publications

(89 citation statements)

References 41 publications

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“…A relatively new method allows the noninvasive measurement of facial skin temperature with thermal cameras. This technique is already successfully applied in emotion perception studies with both humans (e.g., Nhan & Chau, 2010) and macaque monkeys (Kuraoka & Nakamura, 2011), and recently with chimpanzees as well (Kano, Hirata, Deschner, Behringer, & Call, 2016). It is a promising technique from which data even heart rates can be derived, yet it is expensive and dependent on general activity as walking and eating, even though this is to some extent controllable in an experimental setting (Kano et al, 2016).…”

Section: Types Of Stimulimentioning

confidence: 99%

The dot-probe task to measure emotional attention: A suitable measure in comparative studies?

2017

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For social animals, attending to and recognizing the emotional expressions of other individuals is of crucial importance for their survival and likely has a deep evolutionary origin. Gaining insight into how emotional expressions evolved as adaptations over the course of evolution can be achieved by making direct cross-species comparisons. To that extent, experimental paradigms that are suitable for investigating emotional processing across species need to be developed and evaluated. The emotional dot-probe task, which measures attention allocation toward emotional stimuli, has this potential. The task is implicit, and subjects need minimal training to perform the task successfully. Findings in nonhuman primates, although scarce, show that they, like humans, have an attentional bias toward emotional stimuli. However, the wide literature on human studies has shown that different factors can have important moderating effects on the results. Due to the large heterogeneity of this literature, these moderating effects often remain unnoticed. We here review this literature and show that subject characteristics and differences in experimental designs affect the results of the dot-probe task. We conclude with specific recommendations regarding these issues that are particularly relevant to take into consideration when applying this paradigm to study animals.

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Section: Types Of Stimulimentioning

confidence: 99%

The dot-probe task to measure emotional attention: A suitable measure in comparative studies?

2017

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“…Besides PD, other eye-movement metrics (e.g., saccade parameters) are known to reflect stress-related variations (see Benedetto, Pedrotti, & Bridgeman, 2011;Di Stasi, Catena, Cañas, Macknik, & Martinez-Conde, 2013). Efforts are also being made to unobtrusively measure skin temperature and other ANS measures with imaging techniques (see, e.g., Nhan & Chau, 2010;Shastri, Merla, Tsiamyrtzis, & Pavlidis, 2009). …”

Section: Introductionmentioning

confidence: 99%

Automatic Stress Classification With Pupil Diameter Analysis

Pedrotti

Mirzaei

Tedesco³

et al. 2014

International Journal of Human-Computer Interaction

152

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This article proposes a method based on wavelet transform and neural networks for relating pupillary behavior to psychological stress. The proposed method was tested by recording pupil diameter and electrodermal activity during a simulated driving task. Self-report measures were also collected. Participants performed a baseline run with the driving task only, followed by three stress runs where they were required to perform the driving task along with sound alerts, the presence of two human evaluators, and both. Self-reports and pupil diameter successfully indexed stress manipulation, and significant correlations were found between these measures. However, electrodermal activity did not vary accordingly. After training, the four-way parallel neural network classifier could guess whether a given unknown pupil diameter signal came from one of the four experimental trials with 79.2% precision. The present study shows that pupil diameter signal has good discriminating power for stress detection.

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“…정서와 일의 관련성에 관한 연구들이나, 정서와 운전행동에 관한 연구들 을 보면, 일반적으로 부정적인 정서는 안전하지 않은 행동을 하도록 만들며, 사고의 가능성을 증가시킨다고 보고하고 있 다 (Arnett, Offer, & Fine, 1997;Banuls, Carbonell Yaya, Casanoves, & Chisvert, 1996;Chen, Z., Ma, L., & Sen, Y., 2011;Deffenbacher, Lynch, Filetti, Dahlen, & Oetting, 2003 (Bartlett et al, 2006;Chang et al, 2006;Cohn, 2006). 과 혈압(blood pressure), 피부전기반응(electrodermal activity), 피부온도(skin temperature), 호흡률(respiratory rate)을 이용하여 정서상태를 추론한다 (Kim et al, 2004;Liu et al, 2008;Picard et al, 2001 (Liu & Wang, 2011;Merla & Romani, 2007;Rimm-Kaufman & Kagan, 1996;Yoshitomi, 2010 (Jarlier et al, 2011;Khan et al, 2006;Liu & Wang, 2011;Trujillo et al, 2005;Yoshitomi, 2010) (Merla & Romani, 2007;Nakanishi & Imai-Matsumura, 2008;Nhan & Chau, 2010;Kuraoka & Nakamura, 2011;Rimm-Kaufman & Kagan, 1996;Tsiamyrtzi et al, 2007). …”

Section: Introductionunclassified

Emotion Recognition using Facial Thermal Images

Eom¹,

Sohn²

2012

Journal of the Ergonomics Society of Korea

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Objective: The aim of this study is to investigate facial temperature changes induced by facial expression and emotional state in order to recognize a persons emotion using facial thermal images. Background: Facial thermal images have two advantages compared to visual images. Firstly, facial temperature measured by thermal camera does not depend on skin color, darkness, and lighting condition. Secondly, facial thermal images are changed not only by facial expression but also emotional state. To our knowledge, there is no study to concurrently investigate these two sources of facial temperature changes. Method: 231 students participated in the experiment. Four kinds of stimuli inducing anger, fear, boredom, and neutral were presented to participants and the facial temperatures were measured by an infrared camera. Each stimulus consisted of baseline and emotion period. Baseline period lasted during 1min and emotion period 1~3min. In the data analysis, the temperature differences between the baseline and emotion state were analyzed. Eyes, mouth, and glabella were selected for facial expression features, and forehead, nose, cheeks were selected for emotional state features. Results: The temperatures of eyes, mouth, glanella, forehead, and nose area were significantly decreased during the emotional experience and the changes were significantly different by the kind of emotion. The result of linear discriminant analysis for emotion recognition showed that the correct classification percentage in four emotions was 62.7% when using both facial expression features and emotional state features. The accuracy was slightly but significantly decreased at 56.7% when using only facial expression features, and the accuracy was 40.2% when using only emotional state features. Conclusion: Facial expression features are essential in emotion recognition, but emotion state features are also important to classify the emotion. Application: The results of this study can be applied to human-computer interaction system in the work places or the automobiles.

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Classifying Affective States Using Thermal Infrared Imaging of the Human Face

Cited by 127 publications

References 41 publications

The dot-probe task to measure emotional attention: A suitable measure in comparative studies?

The dot-probe task to measure emotional attention: A suitable measure in comparative studies?

Automatic Stress Classification With Pupil Diameter Analysis

Emotion Recognition using Facial Thermal Images

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