  Automated affective computing in the wild setting is a challenging problem in
computer vision. Existing annotated databases of facial expressions in the wild
are small and mostly cover discrete emotions (aka the categorical model). There
are very limited annotated facial databases for affective computing in the
continuous dimensional model (e.g., valence and arousal). To meet this need, we
collected, annotated, and prepared for public distribution a new database of
facial emotions in the wild (called AffectNet). AffectNet contains more than
1,000,000 facial images from the Internet by querying three major search
engines using 1250 emotion related keywords in six different languages. About
half of the retrieved images were manually annotated for the presence of seven
discrete facial expressions and the intensity of valence and arousal. AffectNet
is by far the largest database of facial expression, valence, and arousal in
the wild enabling research in automated facial expression recognition in two
different emotion models. Two baseline deep neural networks are used to
classify images in the categorical model and predict the intensity of valence
and arousal. Various evaluation metrics show that our deep neural network
baselines can perform better than conventional machine learning methods and
off-the-shelf facial expression recognition systems.
