## 5 Results and Discussion  
The evaluation results scientifically validate HUE-Net's effectiveness as a reliable and efficient face recognition model within unconstrained, multi-modal environments. Under all datasets and scenarios, HUE-Net's accuracy in recognition, calibration, and spectral resilience surpassed that of other contemporaneous baseline models alongside computation efficiency, fit for edge deployment. In our full implementation, we use \(M=5\) lightweight stochastic branches within PMB-VM. At inference time, we perform \(T=5\) Monte Carlo forward passes and average the resulting embeddings to compute the final prediction and uncertainty estimate Figure 4.  
Figure 4: Difficult scenarios of face recognition from the IJB-C dataset [17]. Example images show high degrees of variability within the dataset, such as occlusions, extreme poses, lighting artifacts, low resolution, and motion blur. These scenarios demonstrate how face recognition performance suffers under varying conditions, and they illustrate the importance of multi-modal and uncertainty-aware methods like HUE-Net. In the IJB-C benchmark [17], HUE-Net surpassed ArcFace and ElasticFace, which are regarded as SOTA in controlled RGB-only environments, with an overwhelming top-1 accuracy of identification of 95.6%, surpassing ArcFace and ElasticFace's scores of 93.7% and 94.2%, respectively (Table 3). The multispectral fusion attention mechanisms are effective compensators for the pose and illumination extreme variation profile biases, which suggests strong treatment of overpowering ambiguity due to visual fetters in facial geometry. HUE-Net exhibited exceptional cross-modal generalization yet again on the N-SpectralFace dataset [14]. Heavily occluded settings where other models fell short yielded strong results for HUE-Net. Relying solely on subsets of input, such as with the RGB channel removed, HUE-Net achieved a striking recognition accuracy of 92.7%, whereas ArcFace and MS-EVS RetinaFace showed 85.6% and 87.3% in analogous occlusion periods. The Spectral Robustness Score captured these figures, and under partial modality loss, HUE-Net's average performance only dropped by 3.2%, outperforming counterparts with a total average paradigm shift loss exceeding 7% and a glide ratio drop that is standard for RGB-trained architectures. This reflects HUE-Net's ability to seamlessly integrate multi-channel information without excessive dependence on any single channel indicator for forming context Figure 5.  
Beyond accuracy, HUE-Net's exceptional uncertainty estimates further substantiated its well-calibrated predictions. In this respect, HUE-Net achieved the lowest Expected Calibration Error (ECE), at 2.8%, and Brier Score (0.032), surpassing all other models. Inaccurate models stand to benefit most in situations where they need to predict future events alongside uncertainty details. Uncertainty estimate visualization confirmed HUE-Net's performant high-variance assignment to ambiguous or partially occluded inputs and predictive low entropy for clean, frontal views, proving uncertainty outputs reasonable and trustworthy Figure 6.  
Where efficiency is concerned, HUE-Net yielded competitive latency and model size inferences. With only 6.4 million parameters, it needed approximately 180 MFLOPs per inference to operate, grouping it with lightweight architectures like EdgeFace [34]. On edge devices like the NVIDIA Jetson Nano, HUE-Net image processing took only 198 ms, affirming the model's suitability for low-power tasks. These performance metrics, together with deep learning advantages, set HUE-Net apart from bulkier ensemble models that tend to add high latency or memory burden Table 4.