## 5 Results and Discussion
Attention heatmaps revealed that the model, in occluded or low-light scenes, actively shifted attention away from unreliable RGB to NIR edges and dynamic event map contour-driven features. In unobstructed frontal views, attention shifted effectively toward the RGB texture and skin. More importantly, uncertainty visualizations captured the semantic difficulty well: samples assessed as distorted, blurred, or ambiguous registered significantly higher variance in output embeddings Table 5.  
To determine whether simulated event channels contributed meaningful features or merely added redundancy, we trained a version of HUE-Net without event data. The resulting accuracy dropped by 1.9 pp, and the SRS nearly doubled, confirming that temporal contrast encoding via event simulation enhances both robustness and generalization. Importantly, we controlled for parameter count by padding the missing channels, ensuring that this gain was not due to capacity scaling Figure 8.  
Combining these results confirms our primary working hypothesis: the architecture incorporating adaptive attention, variational inference, and multispectral inputs facilitates exceptional performance in real-world face recognition tasks. HUE-Net, in contrast to earlier models, which either depend on static fusion or disregard uncertainty altogether, adapts its representational strategy as a function of the input condition, providing high-confidence predictions that are interpretable and robust across different modalities. To evaluate how the number of branches (M) impacts performance, we tested HUE-Net with M \(\in\) {1, 2, 4, 8}. As expected, both accuracy and calibration improve with larger M, though gains saturate beyond M = 5. Notably, M = 2 already yields strong ECE improvement over deterministic inference (6.8% to 4.2%), with a minimal latency penalty. This suggests that PMB-VM's benefits can be gracefully tuned to deployment constraints, allowing efficient trade-offs between predictive confidence and real-time responsiveness Table 6. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model Variant** & \multicolumn{2}{c}{**N-SpectralFace Accuracy (\%)**} & \multicolumn{1}{p{113.8 pt}}{**SRS \(\downarrow\) (\%)**} & \multicolumn{1}{p{113.8 pt}}{**ECE \(\downarrow\) (\%)**} & \multicolumn{1}{p{113.8 pt}}{**Comment**} \\ \hline HUE-Net (Full) & 94.7 & 3.2 & 2.8 & Full model with ASA, PMB-VM, and consistency \\ \hline – & w/o Adaptive Spectral Attention (ASA) & 92.6 & 7.1 & 5.1 & Reduced robustness under spectral dropout \\ \hline – & w/o PMB-VM & 93.2 & 4.2 & 6.5 & Poor uncertainty calibration \\ \hline – & w/o Spectral Consistency Loss & 91.7 & 6.8 & 4.9 & Higher variance in feature embedding alignment \\ \hline – & w/o Event Channel (RGB + NIR) & 92.8 & 6.3 & 4.1 & Verifies that event-derived maps improve robustness \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study on HUE-Net. We tested different reduction ratios r in the ASA block to assess their impact on model capacity and performance. While r = 2 yields the best accuracy, r = 4 maintains 94.6% accuracy with 6% fewer FLOPs and lower latency, making it a practical choice for edge deployment Table 7.  
We applied temperature scaling (TS) to the logits of the ElasticFace and ArcFace models, using a held-out validation split to optimize the temperature (T). Then we recomputed the calibrated ECE on the IJB-C [17] and N-SpectralFace [14] test sets. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**PMB-VM Branches (M)** & **Top-1 Accuracy (\%)** & **ECE (\%)** & **Inference Latency (Jetson Nano, ms)** \\ \hline M = 1 (deterministic) & 93.3 \(\pm\) 0.3 & 6.8 \(\pm\) 0.3 & 153 \\ \hline M = 2 & 93.9 \(\pm\) 0.3 & 4.2 \(\pm\) 0.3 & 167 \\ \hline M = 4 & 94.5 \(\pm\) 0.2 & 3.1 \(\pm\) 0.2 & 185 \\ \hline M = 5 (default) & 94.7 \(\pm\) 0.3 & 2.8 \(\pm\) 0.2 & 198 \\ \hline M = 8 & 94.8 \(\pm\) 0.2 & 2.6 \(\pm\) 0.2 & 236 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy and calibration vs. number of PMB-VM branches (M).