  This paper presents a light-weight and accurate deep neural model for
audiovisual emotion recognition. To design this model, the authors followed a
philosophy of simplicity, drastically limiting the number of parameters to
learn from the target datasets, always choosing the simplest earning methods:
i) transfer learning and low-dimensional space embedding allows to reduce the
dimensionality of the representations. ii) The isual temporal information is
handled by a simple score-per-frame selection process, averaged across time.
iii) A simple frame selection echanism is also proposed to weight the images of
a sequence. iv) The fusion of the different modalities is performed at
prediction level (late usion). We also highlight the inherent challenges of the
AFEW dataset and the difficulty of model selection with as few as 383
validation equences. The proposed real-time emotion classifier achieved a
state-of-the-art accuracy of 60.64 % on the test set of AFEW, and ranked 4th at
he Emotion in the Wild 2018 challenge.
