  Automatic affect recognition using visual cues is an important task towards a
complete interaction between humans and machines. Applications can be found in
tutoring systems and human computer interaction. A critical step towards that
direction is facial feature extraction. In this paper, we propose a facial
feature extractor model trained on an in-the-wild and massively collected video
dataset provided by the RealEyes company. The dataset consists of a million
labelled frames and 2,616 thousand subjects. As temporal information is
important to the emotion recognition domain, we utilise LSTM cells to capture
the temporal dynamics in the data. To show the favourable properties of our
pre-trained model on modelling facial affect, we use the RECOLA database, and
compare with the current state-of-the-art approach. Our model provides the best
results in terms of concordance correlation coefficient.
