## 2 Related Works  
HUE-Net is developed on an interdisciplinary basis of multispectral face recognition, event-based vision [18], and deep neural network probabilistic modeling [19], along with lightweight edge-deployable architectures [20]. This section discusses pivotal advancements within these fields that guide the framework of our model, while making note of the gaps that remain in practical, resource-limited, and uncertain environments for facial recognition systems (Table 1). In the last decade, the application of deep learning in automated face recognition technology has yielded systems that function with remarkable accuracy in controlled environments. Models like ArcFace [21], ElasticFace [22], and CosFace [23] have rendered exceptional performance by projecting embeddings on hyperspheres and applying angular margin-based loss optimizations. Yet, mostly, these models are restricted to clean, balanced datasets trained under optimal conditions. Their performance in the real world is greatly hindered by exogenous variables such as pose variation, occlusion, lighting shifts, and various sensor artifacts, posing tremendous uncertainty. In addition, these models are tailored for single-modality RGB inputs and lack strategies for spectral variability as well as mechanisms to predict confidently under uncertain qualifiers. To enhance reliability, greater architectural and generative alterations have been made in other studies. For instance, LightCNN [24] pioneered the use of noise-resilient convolutional filters, while the developers of DR-GAN [25] and TP-GAN [26] devised approaches aimed at neutralizing dependence on specific pose and illumination conditions. Even with these adjustments, \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Modalities** & **Uncertainty Modeled** & **Fusion Strategy** & **Backbone Type** & \begin{tabular}{l} **Edge-** \\ **Deployable** \\ \end{tabular} \\ \hline MS-EVS (2024) & RGB + Event & ✗ & Late Fusion & CNN & ✗ \\ \hline HyperFace (2024) & Hyperspectral & ✗ & Deep Fusion & CNN (ResNet) & ✗ \\ \hline BPMB (2024) & RGB & ✓ (Bayesian CNN) & Single Modality & Multi-Branch CNN & ✗ \\ \hline RobustFace++ & RGB & ✓ (MC-Dropout) & Single Modality & ResNet + Ensemble & ✗ \\ \hline FRCSyn (2024) & RGB + Synthetic & ✗ & Frame-Level Fusion & ViT-B & ✗ \\ \hline EdgeFace (2024) & RGB & ✗ & Single Modality & MobileNetV3 & ✓ \\ \hline HUE-Net (Ours) & \
\begin{tabular}{l} RGB + NIR + \\ Event \\ \end{tabular} & ✓ (Variational Branch) & Early Fusion + ASA &
\begin{tabular}{l} CNN–Transformer \\ Hybrid \\ \end{tabular} & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of recent models in face recognition and related domains. these models remain anchored to unadapted deterministic RGB-only pipelines where spectral reliability and predictive uncertainty--critical aspects of dependability for deployment in condition-critical or low-visibility situations--are unaccounted for. Concerning the limitations of RGB systems, the multispectral approach has garnered increased attention from researchers. By adding depth, thermal, or near-infrared (NIR) data, multispectral techniques try to acquire structural and physiological facial features that are invariant to lighting changes. It has been demonstrated that vascular patterns and fine textures obscured to RGB sensors are visible using NIR imaging [27], while thermal imaging provides depictions of sub-dermal heat distributions that are occlusive and supportive to light conditions [28]. Many of these approaches rely on expensive and sophisticated sensor configurations, and as a result, most do not generalize well in the absence or degradation of some modalities. This lack of robustness makes broad deployment impractical. Event-based vision sensors have appeared on the scene as a compelling alternative to traditional frame-based imaging.