  Emotional Intelligence in Human-Computer Interaction has attracted increasing
attention from researchers in multidisciplinary research fields including
psychology, computer vision, neuroscience, artificial intelligence, and related
disciplines. Human prone to naturally interact with computers face-to-face.
Human Expressions is an important key to better link human and computers. Thus,
designing interfaces able to understand human expressions and emotions can
improve Human-Computer Interaction (HCI) for better communication. In this
paper, we investigate HCI via a deep multi-facial patches aggregation network
for Face Expression Recognition (FER). Deep features are extracted from facial
parts and aggregated for expression classification. Several problems may affect
the performance of the proposed framework like the small size of FER datasets
and the high number of parameters to learn. For That, two data augmentation
techniques are proposed for facial expression generation to expand the labeled
training. The proposed framework is evaluated on the extended Cohn-Konade
dataset (CK+) and promising results are achieved.
