## 8 Thermal IR Imaging-Based Affective Computing in HRI  
For decades, a growing interest on the possibility of developing intelligent machines that engage in social interaction has been observed. Many researchers, also in the field of thermal IR imaging, have been enthralled with the HRI appeal and with the possibility of developing robots that are capable of social interactions with humans. The application of thermal IR imaging in this field was firstly investigated by [PERSON] in 2014 [88]. Since then, many studies have been conducted to validate the thermal IR imaging technique for emotion recognition analysis with the aim of applying this technique in the HRI field. However, only few applications have been actually implemented in this area. One of the first attempts was carried out by [PERSON] et al. [89]. In this study, the authors presented the design of a thermal IR imaging-based system mounted on a humanoid robot performing a contact-free measurement of temperature variations across people's faces in a stressful interaction. The results showed a statistically significant interaction between the distance and the gaze direction of the robot and the temperature variation of the nasal and peri-nasal region. This supported the fact that thermal imaging sensors can be successfully employed in embodying robots with physiological sensing capabilities to allow them to become aware of their effect on people, know about their preferences, and build a reactive behavior. [PERSON] et al. reported promising preliminary results in their attempt to determine if a person was trying to deceive a robot through the use of a mobile thermal camera [90]. Instead, [PERSON] et al. evaluated the efficacy of the thermal IR imaging for detecting robot-elicited affective response compared to video-elicited affective response by tracking thermal changes in five ROIs on the subject's face [91]. They studied the interaction effects of condition (robot/video) and emotion (happy/angry) on individual facial ROIs. Although no interaction effects for most ROI temperature slopes were found, a strong, statistically significant effect of the interaction between condition and emotion when evaluating the temperature slope on the nose tip was observed. This result confirms again the assumption that the nasal area is a salient region for emotion detection [32; 67; 92; 93; 94; 95]. Recent studies included applications with more challenging populations such as infants and children. [PERSON] et al. proposed the design of a unique dual-agent system that uses a virtual human and a physical robot to engage 6-12-month-old deaf infants in linguistic interactions. The system was endowed with a perception system that was capable of estimating infant attention and engagement through thermal imaging and eye tracking [96]. This study was part of a larger project designed to develop a system called RAVE (Robot AVatar thermal Enhanced language learning tool). RAVE is aimed to be an augmentative learning tool that can provide linguistic input, in particular visual language inputs, to facilitate language learning during one widely recognized critical developmental period for language (ages 6-12 months [97]) [96; 98; 99; 100; 101]. To this end, thermal IR imaging was used to determine the emotional arousal and attentional valence, providing new knowledge about when infants are most optimally \"Ready to Learn\", even before the onset of language production. This is prominent for infants who might not otherwise receive sufficient language exposure. Of particular concern are deaf babies, many of whom are born to parents who do not know a signed language [96]. Although these studies were very ambitious and fascinating, they were still carried out in constrained laboratory settings, probably implying a not entirely free interaction. To the state of the art, the only two studies performed so far that have concerned HRI applications in a out-of-laboratory context are those reported in Section 7 [86; 87]. [PERSON] et al.