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

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, prov...

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Bibliographic Details
Published in:IEEE transactions on affective computing 2024-07, Vol.15 (3), p.1371-1386
Main Authors: Zhao, Sirui, Tang, Huaying, Mao, Xinglong, Liu, Shifeng, Zhang, Yiming, Wang, Hao, Xu, Tong, Chen, Enhong
Format: Article
Language:English
Subjects:
Affective computing
Benchmarks
Classification
Computer science
Emotion recognition
Emotions
Face recognition
facial action units
facial micro-expression
micro-expression recognition
Psychology
Public safety
Representation learning
Spatiotemporal phenomena
Videos
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Summary: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.
ISSN:1949-3045
1949-3045
DOI:10.1109/TAFFC.2023.3341918