  Emotions play a central role in the social life of every human being, and
their study, which represents a multidisciplinary subject, embraces a great
variety of research fields. Especially concerning the latter, the analysis of
facial expressions represents a very active research area due to its relevance
to human-computer interaction applications. In such a context, Facial
Expression Recognition (FER) is the task of recognizing expressions on human
faces. Typically, face images are acquired by cameras that have, by nature,
different characteristics, such as the output resolution. It has been already
shown in the literature that Deep Learning models applied to face recognition
experience a degradation in their performance when tested against
multi-resolution scenarios. Since the FER task involves analyzing face images
that can be acquired with heterogeneous sources, thus involving images with
different quality, it is plausible to expect that resolution plays an important
role in such a case too. Stemming from such a hypothesis, we prove the benefits
of multi-resolution training for models tasked with recognizing facial
expressions. Hence, we propose a two-step learning procedure, named MAFER, to
train DCNNs to empower them to generate robust predictions across a wide range
of resolutions. A relevant feature of MAFER is that it is task-agnostic, i.e.,
it can be used complementarily to other objective-related techniques. To assess
the effectiveness of the proposed approach, we performed an extensive
experimental campaign on publicly available datasets: \fer{}, \raf{}, and
\oulu{}. For a multi-resolution context, we observe that with our approach,
learning models improve upon the current SotA while reporting comparable
results in fix-resolution contexts. Finally, we analyze the performance of our
models and observe the higher discrimination power of deep features generated
from them.
