  Facial expressions play a fundamental role in human communication. Indeed,
they typically reveal the real emotional status of people beyond the spoken
language. Moreover, the comprehension of human affect based on visual patterns
is a key ingredient for any human-machine interaction system and, for such
reasons, the task of Facial Expression Recognition (FER) draws both scientific
and industrial interest. In the recent years, Deep Learning techniques reached
very high performance on FER by exploiting different architectures and learning
paradigms. In such a context, we propose a multi-resolution approach to solve
the FER task. We ground our intuition on the observation that often faces
images are acquired at different resolutions. Thus, directly considering such
property while training a model can help achieve higher performance on
recognizing facial expressions. To our aim, we use a ResNet-like architecture,
equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild
2 dataset. Not being available a test set, we conduct tests and models
selection by employing the validation set only on which we achieve more than
90\% accuracy on classifying the seven expressions that the dataset comprises.
