  Smiles play a vital role in the understanding of social interactions within
different communities, and reveal the physical state of mind of people in both
real and deceptive ways. Several methods have been proposed to recognize
spontaneous and posed smiles. All follow a feature-engineering based pipeline
requiring costly pre-processing steps such as manual annotation of face
landmarks, tracking, segmentation of smile phases, and hand-crafted features.
The resulting computation is expensive, and strongly dependent on
pre-processing steps. We investigate an end-to-end deep learning model to
address these problems, the first end-to-end model for spontaneous and posed
smile recognition. Our fully automated model is fast and learns the feature
extraction processes by training a series of convolution and ConvLSTM layer
from scratch. Our experiments on four datasets demonstrate the robustness and
generalization of the proposed model by achieving state-of-the-art
performances.
