  Smiles and laughs detection systems have attracted a lot of attention in the
past decade contributing to the improvement of human-agent interaction systems.
But very few considered these expressions as distinct, although no prior work
clearly proves them to belong to the same category or not. In this work, we
present a deep learning-based multimodal smile and laugh classification system,
considering them as two different entities. We compare the use of audio and
vision-based models as well as a fusion approach. We show that, as expected,
the fusion leads to a better generalization on unseen data. We also present an
in-depth analysis of the behavior of these models on the smiles and laughs
intensity levels. The analyses on the intensity levels show that the
relationship between smiles and laughs might not be as simple as a binary one
or even grouping them in a single category, and so, a more complex approach
should be taken when dealing with them. We also tackle the problem of limited
resources by showing that transfer learning allows the models to improve the
detection of confusing intensity levels.
