Research map / Video / Video segmentation and tracking
Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial Dynamics in the Wild
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes IFDD, an implicit facial dynamics disentanglement framework that expands the wavelet lifting scheme into a learnable two-stage module (ISSM splitting + LADM aggregation/disentanglement) to separate emotion-related dynamics from global context for in-the-wild DFER.
Problem
In-the-wild dynamic facial expression recognition suffers from emotion-related frames being temporally and spatially diluted by emotion-irrelevant frames and global context, while prior explicit dynamic-guidance methods are vulnerable to background motion and head movement.
Approach
Expand the wavelet lifting scheme into a fully learnable framework: content-aware, temporal-correlation-driven splitting of frame features into static (global context) and dynamic groups, followed by cross-attention updater/predictor that aggregates and then subtracts global context to yield purified dynamic features, without explicit difference, frequency, optical flow, or landmark guidance.
Key results
- IFDD-3DViT achieves 73.82% WAR and 61.19% UAR on DFEW, outperforming the best supervised method M3DFEL (69.25% WAR) by 6.60% WAR.
- IFDD-3DViT reaches 39.15% UAR / 51.09% WAR on FERV39k and 39.31% UAR / 53.92% WAR on MAFW, outperforming the second-best supervised methods.
- IFDD-2DCNN reaches 56.72% UAR / 70.01% WAR on DFEW with only 1.18M parameters and 3.86 GFLOPs.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add IFDD to MViT-S | MViT-S · DFEW | UAR | Helped |
| Add IFDD to MViT-S | MViT-S · FERV39k | WAR | Helped |
| Add IFDD to MViT-S | MViT-S · MAFW | WAR | Helped |
| Add ISSM and LADM (ablation) | IFDD-3DViT · DFEW | UAR: 57.00 -> 61.19 | Helped |
| Add ISSM and LADM (ablation) | IFDD-3DViT · DFEW | WAR: 68.60 -> 73.82 | Helped |
| Use temporal-correlation interpolation rather than even-odd sampling (ablation) | ISSM · DFEW | UAR: 58.67 -> 61.19 | Helped |
| Use interpolation rather than weighting for temporal-correlation splitting (ablation) | ISSM · DFEW | UAR: 58.53 -> 61.19 | Helped |
| Add mutual relation between feature groups (ablation) | LADM · DFEW | WAR: +1.78% | Helped |
| Add global context loss (ablation) | IFDD-3DViT · DFEW | UAR: 59.58 -> 61.19 | Helped |
| Constrain local average on {T,C} rather than {T,H,W,C} (ablation) | global context loss · DFEW | UAR: 59.74 -> 61.19 | Helped |
| Increase allowable offset range from ±T/4 to ±T/2 (ablation) | ISSM · DFEW | WAR: 73.24 -> 73.82 | Helped |
| Use even-odd rather than midpoint initial indices (ablation) | ISSM · DFEW | UAR: 60.27 -> 61.19 | Helped |
Surprising findings
- Removing the global context loss reduces UAR by 2.63%, and constraining over {T,H,W,C} dimensions performs worse than constraining {T,C}.
- IFDD-2DCNN has fewer parameters (1.18M) than its vanilla MobileNetV2 baseline (1.55M) because the head uses a lower embedding dimension.
Limitations
- Disgust and Fear accuracies remain low, attributed to long-tailed label distribution in DFEW.
- Comparison excludes self-supervised methods, which MAE-DFER surpasses on DFEW; IFDD-3DViT has 37.44M params, far more than the 2D variant.
- Some ablation details (backbone settings, allowable range analysis) are deferred to an extended version.
Experimental setup
Datasets: DFEW FERV39k MAFW
Benchmarks: DFEW FERV39k MAFW
Metrics: UAR WAR
Baselines: C3D 3D ResNet-18 R(2+1)D-18 I3D P3D ResNet18+GRU ResNet18+LSTM Former-DFER STT DPCNet NR-DFERNet GCA+IAL
Builds on: wavelet lifting scheme DAWN LGLFormer PIRNet MViT MobileNetV2
Closest papers by meaning
- OUS: Bridging Scene Context and Facial Features to Overcome the Rigid Cognitive Problem (AAAI 2025 · similarity 0.88)
- D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition. (CVPR 2025 · similarity 0.86)
- Navigating Label Ambiguity for Facial Expression Recognition in the Wild (AAAI 2025 · similarity 0.85)
- Learning Adaptive Spatial Coherent Correlations for Speech-Preserving Facial Expression Manipulation (CVPR 2024 · similarity 0.85)
- DEEPTalk: Dynamic Emotion Embedding for Probabilistic Speech-Driven 3D Face Animation (AAAI 2025 · similarity 0.85)
- Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysis (AAAI 2026 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.