With the advent of Artificial Intelligence (AI) in everyday life, the human-robot interaction is increasingly establishing, as demonstrated for example by the rapid development of different voice personal assistants over the last few years. In this context, the Facial Expression Recognition (FER), is a crucial aspect to design an empathic machine, able to understand the feelings of the facing person and to interact flexibly while considering his emotions. This potentially leads to perform human-like communication that could sensibly improve the human-machine interaction experience. Since the interest in the facial emotion and expression recognition in real settings is spreading faster, it can be determinant having a dataset with human emotions whose manifestation in experimental settings mirrors the phenomena in the real world, rich of various stimuli.