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
this https URL

该研究提出了一个新的动态人脸表情数据库（DFEW）和一种基于聚类表情空间时间特征学习（EC-STFL）框架的解决方案，用于实现 'in-the-wild' 条件下的人脸表情识别技术。实验结果表明，EC-STFL 可以改善现有时空深度神经网络在处理动态 FER 方面的性能。