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A Brain-Inspired Way of Reducing the Network Complexity via Concept-Regularized Coding for Emotion Recognition
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Proposes a brain-inspired dual-pathway FER network where a disentangled conceptual pathway extracts emotion concepts and confidence-filtered concepts regularize a lightweight CNN perceptual pathway for emotion recognition.
Problem
Facial emotion recognition methods are complex, storage-heavy and poorly interpretable; the paper asks whether brain-inspired emotion-concept regularization can yield a lighter, interpretable FER model.
Approach
A conceptual pathway disentangles emotion features from non-emotion (identity) features and assigns adaptive confidence scores; a lightweight perceptual CNN is then regularized by high-confidence emotion concepts (or class-wise averages of high-confidence features when confidence is low) via feature similarity and logit distillation losses.
Key results
- CRPN (11M params) reaches 89.71% on RAF-DB and 63.06% on AffectNet, versus 86.28%/59.52% for the perceptual pathway alone.
- The conceptual pathway alone achieves 90.33% on RAF-DB and 63.97% on AffectNet, and 73.00% on FED-RO when trained on R&A.
- Confidence-based concept selection outperforms plain knowledge distillation (88.36% RAF-DB, 61.63% AffectNet) on both datasets.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CRPN instead of the perceptual pathway alone (ablation) | Perceptual pathway (ResNet-18) · RAF-DB | accuracy: 86.28 -> 89.71 | Helped |
| Use CRPN instead of the perceptual pathway alone (ablation) | Perceptual pathway (ResNet-18) · AffectNet | accuracy: 59.52 -> 63.06 | Helped |
| Add emotion concepts using confidence evaluation rather than ordinary knowledge distillation (ablation) | Perceptual pathway (ResNet-18) · RAF-DB | accuracy: 88.36 -> 89.71 | Helped |
| Add emotion concepts using confidence evaluation rather than ordinary knowledge distillation (ablation) | Perceptual pathway (ResNet-18) · AffectNet | accuracy: 61.63 -> 63.06 | Helped |
| Add LRR + LW CE (ablation) | Conceptual pathway · RAF-DB | accuracy: 87.06 -> 88.02 | Helped |
| Add LRR + LW CE (ablation) | Conceptual pathway · AffectNet | accuracy: 60.01 -> 61.97 | Helped |
| Add Lorth, LCC, and LRR + LW CE together (ablation) | Conceptual pathway · RAF-DB | accuracy: 87.06 -> 90.33 | Helped |
| Add Lorth, LCC, and LRR + LW CE together (ablation) | Conceptual pathway · AffectNet | accuracy: 60.01 -> 63.97 | Helped |
| Increase confidence threshold from 15th to 20th percentile (ablation) | CRPN · RAF-DB | accuracy: 88.40 -> 88.78; 89.11 -> 88.78 | Mixed |
| Increase confidence threshold from 15th to 20th percentile (ablation) | CRPN · AffectNet | accuracy: 61.77 -> 62.14; 62.19 -> 62.14 | Mixed |
Surprising findings
- The larger conceptual pathway (24M params) is only slightly better than the lightweight CRPN on RAF-DB and AffectNet, while CRPN is better on FED-RO (71.00% vs 73.00% is the conceptual path being higher; CRPN is below on FED-RO).
Limitations
- Results are reported on a single CNN backbone family (ResNet) with limited comparison to Transformer-based FER methods, which the authors explicitly did not explore.
- Biological interpretability is argued by analogy to neuroscience literature rather than validated against neural data.
- The IMAGEN in-lab dataset is small and only three emotions are used.
Experimental setup
Datasets: RAF-DB AffectNet FED-RO IMAGEN face task
Benchmarks: RAF-DB AffectNet FED-RO
Metrics: overall sample accuracy parameter count inference run time
Baselines: VGG16 ResNet18 gACNN SPWFA-SE RAN SCN DMUE Knowledge distillation
Builds on: knowledge distillation supervised contrastive learning logit-weighted cross-entropy soft subspace orthogonality
Closest papers by meaning
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