## 5 Results and Discussion
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**Model** & **IJB-C Accuracy (\%)** & **N-SpectralFace Accuracy (\%)** & **Parameters (M)** & **FLOPs (M)** \\ \hline ArcFace (ResNet-100) [21] & 93.7 & x & 65.2 & 4480 \\ \hline ElasticFace & 94.2 & x & 60.0 & 4200 \\ \hline EdgeFace [34] & 92.3 & x & 1.8 & 150 \\ \hline BPMB [15] & 91.9 & x & 19.7 & 2100 \\ \hline MS-EVS RetinaFace & x & 87.3 & 18.2 & 2000 \\ \hline HUE-Net (Ours) & 95.6 & 94.7 & 6.4 & 180 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Face recognition performance across datasets. FLOPs and inference times reflect the computational cost of the forward pass only, excluding data preprocessing. Latency measured with batch size = 1 on NVIDIA RTX 2080 GPU. An ablation analysis aided in verifying the validity of HUE-Net's three core components. The model without the Adaptive Spectral Attention module showed a reduction of 2.1% in recognition accuracy on N-SpectralFace [14], while the SRS increased by almost 4%, supporting the assumption that spectral modulation significantly aids in scene-specific reliability tailoring Figure 7. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **SRS \(\downarrow\) (\%)** & **ECE \(\downarrow\) (\%)** & **Brier Score \(\downarrow\)** & **Inference Time (GPU, ms)** & **Inference Time (Jetson Nano, ms)** \\ \hline ArcFace [21] & 7.8 & 8.2 & 0.049 & 98 & \textgreater{}1000 (not suitable) \\ \hline ElasticFace & 6.9 & 7.4 & 0.045 & 93 & \textgreater{}800 \\ \hline EdgeFace [34] & 5.6 & 5.9 & 0.042 & 32 & 187 \\ \hline BPMB [15] & 4.9 & 5.1 & 0.037 & 105 & 290 \\ \hline MS-EVS RetinaFace & 5.8 & 6.5 & 0.040 & 64 & 245 \\ \hline HUE-Net (Ours) & 3.2 & 2.8 & 0.032 & 41 & 198 \\ \hline \hline \end{tabular}
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Table 4: Robustness and calibration metrics. Figure 5: Exemplary cross-spectral face recognition results from HUE-Net. All rows show the respective RGB, NIR, and event frame simulation and their fused outputs. Successful face recognition and detection across all modalities are highlighted within the red bounding boxes. The figure showcases HUE-Net’s strengths in low-light, occluded, and motion blur scenarios, reflecting harsh illumination extremes due to spectral complementarity. The edges and motion information are integrated in the event-based modality, while NIR provides reliable capture in dark or heavily backlit scenes. Figure 6: Standard deviation of identity embeddings varied across PMB-VM branches with separate angular distance processing. The plot shows the standard deviation concerning angular distance for stochastic identity embeddings under visual conditions. While clean inputs show low variance (high confidence), occluded or blurred conditions produce greater uncertainty. The red dashed line marks an empirical uncertainty threshold (0.3) above which predictions can be considered for deferred processing or flagged as unreliable. Figure 7: Spectral attention weights across various conditions: visual and environmental. Rows represent different input conditions (clean, low light, occlusion, motion blur), while columns show the attention weight assigned to each modality by the Adaptive Spectral Attention (ASA) module. For example, during low-light conditions, the network relies more on NIR and event-based cues, whereas in clean inputs, RGB is prioritized. This type of adaptivity improves robustness and interpretability. The exclusion of the PMB-VM variational branch resulted in ECE improving from 2.8% to 6.5%, illustrating the importance of predictive uncertainty modeling in decision-making and reliability. Disabling the spectral consistency loss component also caused a drop in performance during modality dropout scenarios, highlighting the importance of regularization provided by diverse spectral inputs. These conclusions were further validated by qualitative visualizations.