.png){kind=icon}
返回
Micro-expression recognition using dual-view self-supervised contrastive learning with intensity perception
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2024.129142.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Micro-expressions, as indicators of true emotions, have significant applications in medical care and public safety. These expressions are characterized by their short duration, low intensity, and localized occurrence. These characteristics lead to the small sample problem in micro-expressions, making feature learning challenging and limiting the improvement of recognition performance. To address this issue, we propose a multimodal contrastive learning pre-training model based on Action Unit (AU) intensity perception. We conducted an experiment to determine the minimum threshold for recognizing facial expressions. Using this threshold, we filtered a large volume of unsupervised samples. The first stage involves unsupervised multimodal contrastive learning, where the model learns from differences in facial actions across various modalities. Subsequently, the model is trained on the micro-expression recognition task using a small amount of labeled data, overcoming the limitations of small sample sizes. Comparative experiments using the MEGC2019-CD and the multimodal dataset CAS(ME)3 datasets demonstrate the superiority of our method. Our method is available at https: //github.com/MELABIPCAS/DVSCL.git.
Keyword:
Micro-expression
Small sample size problem
Contrastive learning
Self-supervised learning
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
.png){kind=icon}
分享收藏Discriminative Spatiotemporal Local Binary Pattern with Revisited Integral Projection for Spontaneous Facial Micro-Expression Recognition用于自发面部微表情识别的具有重访积分投影的区分性时空局部二值模式
OAAI
OAAI分享收藏
查看更多内容