## 1 Introduction
Relevantly to the topic of this study, IR imaging allows to infer the peripheral autonomic activity through the modulation of the cutaneous temperature, which is a known expression of the psycho-physiological state of the subject [19; 20]. In fact, experienced emotions, including stress or fatigue, can produce changes in skin temperature [21; 22; 23]. Particularly, there is great attention in this research field on stress and mental workload monitoring using thermal IR imaging. [PERSON] et al. [24] studied computer users' stress, reporting an increased blood flow in the frontal vessels of the forehead during stressful situations. The thermal metric was shown to be correlated with stress levels in 12 participants performing a Stroop test (\(r\) = 0.9, excluding one outlier). [PERSON] and colleagues [25] tried to assess the stress level by measuring transient respiratory responses on the perinasal area using thermal IR imaging. These metrics proved to be a good indicator of stress response, because they were sympathetically driven. The authors applied this approach in the context of surgical training, finding a very high correlation between the GSR (galvanic skin response) and the thermal measurement on the finger (\(r\) = 0.968) and on the perinasal region (\(r\) = 0.943). [PERSON] et al. [26] used thermal IR imaging to assess affective training times by monitoring the cognitive load through facial temperature changes. Learning proficiency patterns were based on an alphanumeric task. Significant correlations, ranging from \(r\) = 0.88 to \(r\) = 0.96, were found between the nose tip temperature and the response time,accuracy, and the Modified Cooper Harper Scale ratings. [PERSON] et al. [27] presented a system for the estimation of cognitive workload levels based on the analysis of facial skin temperature. Beyond thermal infrared imaging of the face, the system relied on head pose estimation, measurement of the temperature variation across regions of the face, and an artificial neural network classifier. The system was capable of accurately classifying mental workload into high, medium, and low levels 81% of the time. Given the advantages of the use of thermography in driver state monitoring, a relevant number of scientific works on this research field are available. Most of these publications concern driver drowsiness/fatigue monitoring and emotional state detection. [PERSON] et al. [28] investigated driver drowsiness by analyzing the breathing function, monitored by thermography. The authors observed a significant decrease of driver respiration rate from extreme drowsiness to wakefulness conditions. Moreover, [PERSON] et al. [29] presented an approach for the evaluation of driver's fatigue, based on yawn detection using thermal imaging. [PERSON] et al. [30] demonstrated the feasibility of discriminating emotions (e.g., fear versus no fear) by means of thermal imaging, assessing the forehead temperature as indicative for the emotional dimension of drivers' fear. Focusing on driver stress monitoring, [PERSON] et al. [31] combined measures from facial skin temperature and hemodynamic variables. The authors observed an increase of sympathetic activity, peripheral vasoconstriction, hence, a significant decrease in peripheral skin temperature during monotonous driving simulation. Basing on differential skin temperatures between peripheral (i.e., nose tip) and truncal parts of the face (i.e., cheeks, jaw, and forehead), they were able to assess an index of a driver's stress. More recently, [PERSON] et al. [32] studied the effects of cognitive, emotional, sensorimotor, and mixed stressors on driver arousal and performance with respect to a baseline of 59 drivers in a simulation experiment. Perinasal perspiration, revealed by thermal imaging, together with the measure of steering angle and the range of lane departures on the left and right side of the road, showed a more dangerous driving condition in the case of sensorimotor and mixed stressors with respect to the baseline condition.