  Recently, facial expression recognition (FER) in the wild has gained a lot of
researchers' attention because it is a valuable topic to enable the FER
techniques to move from the laboratory to the real applications. In this paper,
we focus on this challenging but interesting topic and make contributions from
three aspects. First, we present a new large-scale 'in-the-wild' dynamic facial
expression database, DFEW (Dynamic Facial Expression in the Wild), consisting
of over 16,000 video clips from thousands of movies. These video clips contain
various challenging interferences in practical scenarios such as extreme
illumination, occlusions, and capricious pose changes. Second, we propose a
novel method called Expression-Clustered Spatiotemporal Feature Learning
(EC-STFL) framework to deal with dynamic FER in the wild. Third, we conduct
extensive benchmark experiments on DFEW using a lot of spatiotemporal deep
feature learning methods as well as our proposed EC-STFL. Experimental results
show that DFEW is a well-designed and challenging database, and the proposed
EC-STFL can promisingly improve the performance of existing spatiotemporal deep
neural networks in coping with the problem of dynamic FER in the wild. Our DFEW
database is publicly available and can be freely downloaded from
https://dfew-dataset.github.io/.
