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Spontaneous Facial Expressions and Micro-expressions Coding: From Brain to Face
Zizhao Dong1, Gang Wang2, Shaoyuan Lu1,3
1Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Frontiers in Psychology
|January 21, 2022
Summary
This study simplifies facial expression analysis by linking facial muscles to emotion coding. It provides a foundational understanding for recognizing emotions and micro-expressions from videos, making it accessible to more researchers.
Area of Science:
- Neuroscience and Psychology
- Computer Science and Artificial Intelligence
Background:
- Facial expressions are key to human emotion perception.
- Current video-based expression analysis requires expert Action Unit (AU) coding knowledge, posing a significant barrier.
Purpose of the Study:
- To simplify the process of analyzing facial expressions and micro-expressions in videos.
- To establish a foundational understanding of the relationship between facial muscles, AUs, and emotions for broader accessibility.
Main Methods:
- Deconstructing facial muscle movements from the motor cortex.
- Systematically analyzing the relationship among facial muscles, AUs, and emotions using data-driven methods and expert experience.
- Discussing the facial motor cortical network, facial nucleus, and associated motor systems.
Main Results:
- Derived a data-driven relationship between AUs and emotions from 5,000 images (RAF-AU database).
- Established physiological theory supporting AU labeling of emotions by incorporating facial muscle movement patterns.
- Presented a detailed process for emotion labeling and AU detection/recognition.
Conclusions:
- The study provides a simplified, principle-based approach to understanding facial expression coding.
- This research facilitates broader participation in the analysis of spontaneous expressions and micro-expressions from video data.
- Future research directions for video coding of expressions and micro-expressions are proposed.
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