MCCA-VNet: A Vit-Based Deep Learning Approach for Micro-Expression Recognition Based on Facial Coding
Dehao Zhang, Tao Zhang, Haijiang Sun, Yanhui Tang, Qiaoyuan Liu · 2024
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Description
Dehao Zhang and co-authors read micro-expressions, the split-second face shifts that leak true feeling, in a 2024 paper. Such reads aid counseling and diagnosis, since micro-expressions are harder to fake than full ones. Past systems leaned on motion flow or plain Transformers and missed how face landmarks relate in space.
Their model, MCCA-VNet, first pulls change features from the face stream. Channel and spatial attention are then woven into a Vision Transformer so the model weighs both what changed and where. On three public sets, and on a merged set, the model set the best scores on both grading scales used. Honest emotion reads could help clinics screen distress that patients mask.
Catalog record
| CREATOR | Dehao Zhang, Tao Zhang, Haijiang Sun, Yanhui Tang, Qiaoyuan Liu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |