## 3 The Materials and Methods
### Adaptive Spectral Attention  
In the case of real-life scenarios of face recognition, context-centric event data, RGB, and near-infrared (NIR) capture images are used in different contexts. Each context has limitations that may at times differ significantly. These perspectives differ in informative value as well. Under harsh lighting conditions, RGB channels may suffer from overexposure which masks feature depiction. On the other hand, NIR lacks lighting resilience, but in extreme indoor natural lighting, it is prone to passive infrared disruption. Although event-based information is temporally rich, it is static and therefore sparse and noisy. Thus, treating all modalities simultaneously may strengthen the model's identity representation capabilities, but it does not provide robust extraction. This challenge is addressed through the Adaptive Spectral Attention (ASA) mechanism, which allows HUE-Net to adjust specific alteration amplifications to contextual relevance in a dramatic contextual role-based framework. The attention mechanism acts as a smart dynamic gate system: it flexibly determines where spatial focus should be applied and what prioritization at the contextual level depends on. The ASA process begins by capturing global context from the fused input tensor. \(X\in R^{H\times W\times C}\) denotes the fused input where \(C=6\) channels correspond to RGB, NIR, and two event-based maps. To summarize the spectral context, a global average pooling is applied across spatial dimensions:  
\[s_{c}=\frac{1}{H\times W}{\sum}_{i=1}^{H}\sum_{j=1}^{W}X_{i,j,c},\ \ for\ c=1,\ldots,C \tag{12}\]  
This produces a channel descriptor \(s\in R^{C}\) that captures modality-specific global statistics. To model non-linear interactions between these spectral channels, we apply a two-layer gating network, essentially a bottlenecked multi-layer perceptron (MLP):  
\[a_{spec}=\sigma(W_{2}\times\delta(W_{1}\times s)) \tag{13}\]  
Here, \(W_{1}\in R^{\frac{C}{\gamma}\times C}\), \(W_{2}\in R^{C\times\frac{C}{\gamma}}\), \(\delta(\cdot)\) is the ReLU activation function, and \(\sigma(\cdot)\) is the sigmoid function. The reduction ratio r controls the bottleneck width, typically set to 2 in our configuration. The output \(a_{spec}\in R^{C}\) is a learned set of attention weights that dynamically scale each modality's contribution. These attention weights are broadcast and applied to the corresponding input channels:  
\[\widetilde{X}_{i,j,c}=a_{spec}(c)\times X_{i,j,c} \tag{14}\]  
This operation yields a recalibrated tensor \(\widetilde{X}\in R^{H\times W\times C}\), where less reliable spectral sources are suppressed and more informative ones are emphasized. To build on this modulation, especially when the modality quality differs among facial areas, we add an optional spatial refinement stage. This block obtains a spectral-spatial attention map \(\alpha\in R^{H\times W\times C}\) through the use of depthwise separable convolutions followed by a channel softmax at each spatial position:  
\[\alpha_{i,j,c}=\frac{exp\left(K_{c}*\widetilde{X}_{c}\right)}{\sum_{c^{{}^{ \prime}}}^{C}exp\left(K_{c^{{}^{\prime}}}*\widetilde{X}_{c^{{}^{\prime}}}\right)} \tag{15}\]  
where * denotes a convolution with learned kernel \(K_{c}\). The final attended representation is then computed via elementwise multiplication: \(X_{att}=\alpha\odot\widetilde{X}\). This operation makes attention to information both modality-aware and spatially adaptive, for reliable information distributed over the face. All parts of the ASA module are differentiable and are thus trained in conjunction with the rest of the network. The module aims to form modality preferences that are learned to statistically align with the scenes, adaptive to the statistical properties of the scene in a certain manner. For example, in low-light scenes, ASA progressively enhances the weights assigned to NIR and event channels while suppressing the contributions from RGB. Conversely, in bright indoor conditions with stable illumination, RGB could become dominant again.