DFME: A New Benchmark for Dynamic Facial Micro-Expression Recognition

计算机科学 水准点(测量) 面部表情 领域(数学) 人工智能 稀缺 机器学习 情感计算 特征(语言学) 表达式(计算机科学) 面子(社会学概念) 数据库 语言学 哲学 数学 大地测量学 纯数学 程序设计语言 经济 微观经济学 地理 社会科学 社会学
作者
Sirui Zhao,Huaying Tang,Xinglong Mao,Shifeng Liu,Yiming Zhang,Hao Wang,Tong Xu,Enhong Chen
出处
期刊:IEEE Transactions on Affective Computing [Institute of Electrical and Electronics Engineers]
卷期号:15 (3): 1371-1386 被引量:5
标识
DOI:10.1109/taffc.2023.3341918
摘要

One of the most important subconscious reactions, micro-expression (ME), is a spontaneous, subtle, and transient facial expression that reveals human beings' genuine emotion. Therefore, automatically recognizing ME (MER) is becoming increasingly crucial in the field of affective computing, providing essential technical support for lie detection, clinical psychological diagnosis, and public safety. However, the ME data scarcity has severely hindered the development of advanced data-driven MER models. Despite the recent efforts by several spontaneous ME databases to alleviate this problem, there is still a lack of sufficient data. Hence, in this paper, we overcome the ME data scarcity problem by collecting and annotating a dynamic spontaneous ME database with the largest current ME data scale called DFME (Dynamic Facial Micro-expressions). Specifically, the DFME database contains 7,526 well-labeled ME videos spanning multiple high frame rates, elicited by 671 participants and annotated by more than 20 professional annotators over three years. Furthermore, we comprehensively verify the created DFME, including using influential spatiotemporal video feature learning models and MER models as baselines, and conduct emotion classification and ME action unit classification experiments. The experimental results demonstrate that the DFME database can facilitate research in automatic MER, and provide a new benchmark for this field. DFME will be published via https://mea-lab-421.github.io.
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