  Smile veracity classification is a task of interpreting social interactions.
Broadly, it distinguishes between spontaneous and posed smiles. Previous
approaches used hand-engineered features from facial landmarks or considered
raw smile videos in an end-to-end manner to perform smile classification tasks.
Feature-based methods require intervention from human experts on feature
engineering and heavy pre-processing steps. On the contrary, raw smile video
inputs fed into end-to-end models bring more automation to the process with the
cost of considering many redundant facial features (beyond landmark locations)
that are mainly irrelevant to smile veracity classification. It remains unclear
to establish discriminative features from landmarks in an end-to-end manner. We
present a MeshSmileNet framework, a transformer architecture, to address the
above limitations. To eliminate redundant facial features, our landmarks input
is extracted from Attention Mesh, a pre-trained landmark detector. Again, to
discover discriminative features, we consider the relativity and trajectory of
the landmarks. For the relativity, we aggregate facial landmark that
conceptually formats a curve at each frame to establish local spatial features.
For the trajectory, we estimate the movements of landmark composed features
across time by self-attention mechanism, which captures pairwise dependency on
the trajectory of the same landmark. This idea allows us to achieve
state-of-the-art performances on UVA-NEMO, BBC, MMI Facial Expression, and SPOS
datasets.
