Facial Expression Recognition in the Wild using Rich Deep Features
It addresses the problem of recognizing facial expressions in real-world, unconstrained settings for applications in computer vision, though it appears incremental.
The paper tackles facial expression recognition in the wild by fusing rich deep features with domain knowledge, achieving state-of-the-art results on benchmark datasets CK and TFE and a new natural expression dataset.
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