Facial Expression Recognition in the Wild using Rich Deep Features

TL;DR

This paper introduces a novel deep learning approach for facial expression recognition in real-world conditions, combining rich features with domain knowledge, and demonstrates state-of-the-art results on benchmark and new natural-expression datasets.

Contribution

The paper proposes a new method that fuses deep features with facial patch encoding and introduces a natural-expression dataset for improved real-world recognition.

Findings

Achieves state-of-the-art accuracy on CK and TFE datasets.

Introduces a new dataset with natural facial expressions.

Demonstrates effectiveness of rich deep features combined with domain knowledge.

Abstract

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