## 1 Introduction
The main approaches to objectively measure these emotional signs rely on the observation of the face, gestures or body posture, and on measurements of physiological parameters through contact sensors [15; 16]. Visual observation has the advantage of being completely non-invasive and only moderately intrusive. In fact, whereas a camera is being considered an intrusion into privacy, people are inclined to accept and forget it once they get used to it and see a value in it [17]. Furthermore, the greatest advantage of using the visual domain for emotion recognition is that it relies on years of study and cutting-edge algorithms developed for face identification and facial expressions analysis. The disadvantage of the visual approach is that people are prone to avoid facial expressions when interacting with technical systems [18]. In fact, the facial display of emotional states characterizes the human-to-human interaction, and it was acquired through evolution to facilitate communication and to influence others' actions [19; 20]. Since machines do not still respond to emotional expressions, humans have not yet learned to use this channel when communicating with a robot. On the contrary, measuring physiological parameters has the advantage of evaluating features that people cannot control or mask [21]. Therefore, they may deliver much more reliable data about the emotional symptoms than visual channels. Physiological parameters such as heart rate, skin temperature, and electrodermal activity are very simple to acquire and analyze. The drawback of working with physiological readings is that they are mostly obtained through contact sensors [22]. Direct contact with the person's skin requires the willingness to correctly wear the device. Novel wearable sensors have to meet various technological requirements (e.g., reliability, robustness, availability, and quality of data), which are often very difficult to obtain. Finally, the time required for sensors placing is not negligible. In recent years, thermal InfraRed (IR) imaging has been used for Affective Computing [23; 24; 25], and it exploits the advantages of both visual and physiology measuring approaches, as well as overcoming their drawbacks. Thermal IR imaging-based Affective Computing is a breakthrough technology that enables monitoring human physiological parameters and Autonomic Nervous System (ANS) activity in a non-contact manner and without subject's constraining [26]. Although thermal IR imaging has been widely used for human emotion recognitions [27; 28; 29; 30; 31; 32; 33; 34; 35], its application in HRI implies overcoming ambitious and entirely new challenges. The most important ones are real-time monitoring and real-life applications. To this end, the use of a mobile and ideally miniaturized technology is essential to bridge the gap between laboratory and real-world applications. In addition, since robots should be commercially available, they need to include low-cost technology, which implies facing further issues such as low resolution and signal-to-noise ratio. In this survey, the importance of facilitating HRI in different aspect of everyday life is firstly highlighted. The state-of-the-art of affective state recognition through thermal IR imaging is briefly reviewed. Afterwards, the above-mentioned challenges are examined, indicating what has been done so far and suggesting further development to ensure its future efficient use in the field. Finally, the possible impact of this technology for HRI applications in infants, children, and adults is highlighted.