  Facial Expression Recognition is an active area of research in computer
vision with a wide range of applications. Several approaches have been
developed to solve this problem for different benchmark datasets. However,
Facial Expression Recognition in the wild remains an area where much work is
still needed to serve real-world applications. To this end, in this paper we
present a novel approach towards facial expression recognition. We fuse rich
deep features with domain knowledge through encoding discriminant facial
patches. We conduct experiments on two of the most popular benchmark datasets;
CK and TFE. Moreover, we present a novel dataset that, unlike its precedents,
consists of natural - not acted - expression images. Experimental results show
that our approach achieves state-of-the-art results over standard benchmarks
and our own dataset
