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Recently, facial expression recognition (FER) in the wild has gained a lot ofresearchers' attention because it is a valuable topic to enable the FERtechniques to move from the laboratory to the real applications. In this paper,we focus on this challenging but interesting topic and make contributions fromthree aspects. First, we present a new large-scale 'in-the-wild' dynamic facialexpression database, DFEW (Dynamic Facial Expression in the Wild), consistingof over 16,000 video clips from thousands of movies. These video clips containvarious challenging interferences in practical scenarios such as extremeillumination, occlusions, and capricious pose changes. Second, we propose anovel method called Expression-Clustered Spatiotemporal Feature Learning(EC-STFL) framework to deal with dynamic FER in the wild. Third, we conductextensive benchmark experiments on DFEW using a lot of spatiotemporal deepfeature learning methods as well as our proposed EC-STFL. Experimental resultsshow that DFEW is a well-designed and challenging database, and the proposedEC-STFL can promisingly improve the performance of existing spatiotemporal deepneural networks in coping with the problem of dynamic FER in the wild. Our DFEWdatabase is publicly available and can be freely downloaded fromhttps://dfew-dataset.github.io/.

DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild