Drones and other unmanned aerial vehicles (UAVs) are increasingly autonomous. This makes it possible to significantly reduce the number of operators at the controls during pre-programmed missions. Moreover, human factors remain of particular concern with regard to the safety of UAV flights, since autonomous systems, which are increasingly diversified and whose operations are more and more complex, require longer periods of control and supervision. As a result, human factors now account for more than two-thirds of the causes of UAV incidents and accidents. Continuous monitoring of the emotional state and stress of operators would limit mental overload during long work periods and minimise opportunities for critical errors. There are several methods of such monitoring, such as heart rate sensors, blood tests, etc., which are available. However, most of them are intrusive and therefore less practical. This is why several researchers are interested in developing other stress assessment techniques, particularly based on the psychological indicators that can be revealed by facial emotions. With recent advances in artificial intelligence (AI) and computer vision, it is becoming possible to combine the analysis of facial emotions with the evaluation of the emotional state and the operator. The integration of AI could eventually make it possible to quickly and adequately assess the operator’s state, limit the impact of human factors on incidents, and even optimise collaboration between the operator and the UAV.