Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial Dynamics in the Wild

• URL: http://arxiv.org/abs/2412.13168v2
• Date: Wed, 18 Dec 2024 09:47:15 GMT
• Title: Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial Dynamics in the Wild
• Authors: Xingjian Wang, Li Chai,
• Abstract summary: In-the-wild dynamic facial expression recognition (DFER) encounters a significant challenge in recognizing emotion-related expressions.<n>We propose a novel Implicit Facial Dynamics Disentanglement framework (IFDD)<n>IFDD disentangles emotion-related dynamic information from emotion-irrelevant global context in an implicit manner.
• Score: 3.3905929183808796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Code is available at https://github.com/CyberPegasus/IFDD.

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