  In this paper, we propose an approach for Facial Expressions Recognition
(FER) based on a deep multi-facial patches aggregation network. Deep features
are learned from facial patches using deep sub-networks and aggregated within
one deep architecture for expression classification . Several problems may
affect the performance of deep-learning based FER approaches, in particular,
the small size of existing FER datasets which might not be sufficient to train
large deep learning networks. Moreover, it is extremely time-consuming to
collect and annotate a large number of facial images. To account for this, we
propose two data augmentation techniques for facial expression generation to
expand FER labeled training datasets. We evaluate the proposed framework on
three FER datasets. Results show that the proposed approach achieves
state-of-art FER deep learning approaches performance when the model is trained
and tested on images from the same dataset. Moreover, the proposed data
augmentation techniques improve the expression recognition rate, and thus can
be a solution for training deep learning FER models using small datasets. The
accuracy degrades significantly when testing for dataset bias.
