Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial Dynamics in the Wild
Abstract
In-the-wild dynamic facial expression recognition (DFER) encounters a significant challenge in recognizing emotion-related expressions, which are often temporally and spatially diluted by emotion-irrelevant expressions and global context. Most prior DFER methods directly utilize coupled spatiotemporal representations that may incorporate weakly relevant features with emotion-irrelevant context bias. Several DFER methods highlight dynamic information for DFER, but following explicit guidance that may be vulnerable to irrelevant motion. In this paper, we propose a novel Implicit Facial Dynamics Disentanglement framework (IFDD). Through expanding wavelet lifting scheme to fully learnable framework, IFDD disentangles emotion-related dynamic information from emotion-irrelevant global context in an implicit manner, i.e., without exploit operations and external guidance. The disentanglement process contains two stages. The first is Inter-frame Static-dynamic Splitting Module (ISSM) for rough disentanglement estimation, which explores inter-frame correlation to generate content-aware splitting indexes on-the-fly. We utilize these indexes to split frame features into two groups, one with greater global similarity, and the other with more unique dynamic features. The second stage is Lifting-based Aggregation-Disentanglement Module (LADM) for further refinement. LADM first aggregates two groups of features from ISSM to obtain fine-grained global context features by an updater, and then disentangles emotion-related facial dynamic features from the global context by a predictor. Extensive experiments on in-the-wild datasets have demonstrated that IFDD outperforms prior supervised DFER methods with higher recognition accuracy and comparable efficiency.
BibTeX
@article{Wang_Chai_2025, title={Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial Dynamics in the Wild}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/32859}, DOI={10.1609/aaai.v39i8.32859}, abstractNote={In-the-wild dynamic facial expression recognition (DFER) encounters a significant challenge in recognizing emotion-related expressions, which are often temporally and spatially diluted by emotion-irrelevant expressions and global context.
Most prior DFER methods directly utilize coupled spatiotemporal representations that may incorporate weakly relevant features with emotion-irrelevant context bias.
Several DFER methods highlight dynamic information for DFER, but following explicit guidance that may be vulnerable to irrelevant motion.
In this paper, we propose a novel Implicit Facial Dynamics Disentanglement framework (IFDD).
Through expanding wavelet lifting scheme to fully learnable framework, IFDD disentangles emotion-related dynamic information from emotion-irrelevant global context in an implicit manner, i.e., without exploit operations and external guidance.
The disentanglement process contains two stages.
The first is Inter-frame Static-dynamic Splitting Module (ISSM) for rough disentanglement estimation, which explores inter-frame correlation to generate content-aware splitting indexes on-the-fly.
We utilize these indexes to split frame features into two groups, one with greater global similarity, and the other with more unique dynamic features.
The second stage is Lifting-based Aggregation-Disentanglement Module (LADM) for further refinement.
LADM first aggregates two groups of features from ISSM to obtain fine-grained global context features by an updater, and then disentangles emotion-related facial dynamic features from the global context by a predictor.
Extensive experiments on in-the-wild datasets have demonstrated that IFDD outperforms prior supervised DFER methods with higher recognition accuracy and comparable efficiency.}, number={8}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Wang, Xingjian and Chai, Li}, year={2025}, month={Apr.}, pages={7970-7978} }