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Facial Expression Recognition is an active area of research in computervision with a wide range of applications. Several approaches have beendeveloped to solve this problem for different benchmark datasets. However,Facial Expression Recognition in the wild remains an area where much work isstill needed to serve real-world applications. To this end, in this paper wepresent a novel approach towards facial expression recognition. We fuse richdeep features with domain knowledge through encoding discriminant facialpatches. 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 showthat our approach achieves state-of-the-art results over standard benchmarksand our own dataset

Facial Expression Recognition in the Wild using Rich Deep Features