  Understanding the facial expressions of our interlocutor is important to
enrich the communication and to give it a depth that goes beyond the explicitly
expressed. In fact, studying one's facial expression gives insight into their
hidden emotion state. However, even as humans, and despite our empathy and
familiarity with the human emotional experience, we are only able to guess what
the other might be feeling. In the fields of artificial intelligence and
computer vision, Facial Emotion Recognition (FER) is a topic that is still in
full growth mostly with the advancement of deep learning approaches and the
improvement of data collection. The main purpose of this paper is to compare
the performance of three state-of-the-art networks, each having their own
approach to improve on FER tasks, on three FER datasets. The first and second
sections respectively describe the three datasets and the three studied network
architectures designed for an FER task. The experimental protocol, the results
and their interpretation are outlined in the remaining sections.
