## 3 The Materials and Methods
### Multispectral Event Fusion  
To achieve reliable facial recognition in highly unconstrained contexts like those with extreme lighting, occlusions, or injuries deforming the face, it is essential to exploit imaging cues beyond the RGB spectrum. HUE-Net employs a multifusion processing model through a multispectral event fusion input strategy, which combines three data modalities: RGB, NIR, and simulated event representations. This fusion aims to increase the spectral bandwidth as well as the temporal responsiveness of the model. Although RGB and NIR modalities were acquired via asynchronous sequential capture (\(\sim\)10 ms interval), the static nature of subjects and controlled capture timing allowed us to treat these frames as quasi-synchronous. We performed a two-stage alignment: a coarse affine transformation using mutual information, followed by optical flow-based warping to correct local motion. This strategy avoids assuming perfect temporal registration and provides accurate alignment even in the presence of small inter-frame shifts. This enables the model to perform better under difficult imaging conditions (Figure 2). HUE-Net receives an input tensor of six channels, which are arranged as follows:  
\[X=\left[R,G,B,NIR,E^{RGB},E^{NIR}\right]\in R^{H\times W\times 6} \tag{1}\]  
where \(R,G,B\) correspond to the visible channels; \(NIR\) denotes the near-infrared channel, captured at a wavelength of \(\sim\)850 nm; and \(E^{RGB},E^{NIR}\) represent the RGB-based and \(NIR\)-based simulated event channels, respectively. All channels are spatially registered and normalized to a dynamic range of [0, 1] prior to fusion. In HUE-Net, each modality has a specific and complementary function for preserving facial recognition under diverse and difficult conditions. From the RGB input, photometric data such as skin color and texture, as well as facial features, are recognizable to humans and highly effective in proper lighting. The NIR channel, on the other hand, goes deeper into the skin layers and is much more useful in low-light or backlit situations because it is less sensitive to illumination variation. The event-based channels, in the meantime, detect temporal contrast changes in pixel intensity over time. They work well in areas where motion blur, flickering lights, or fine-grained obstruction occurs. All these modalities enhance and enrich perception and structure and, together with HUE-Net, offer robust face recognition over a wide range of imaging disturbances. Because of the limited availability of real datasets based on events and facial recognition, we employ a simulation-based approximation influenced by earlier studies in the field of neuro-morphic vision. Givena pair of temporally adjacent frames \(I_{t}\) and \(I_{t-\delta}\), the event frame E is computed via a logarithmic differencing mechanism:  
\[E^{c}=(x,y)=\left\{ \begin{array}{ll}1,&logI^{c}_{t}(x,y)-logI^{c}_{t-\delta}(x,y)>\theta^{+}\\ -1&logI^{c}_{t}(x,y)-logI^{c}_{t-\delta}(x,y)>-\theta^{-}\\ 0&otherwise\end{array} \right. \tag{2}\]  
where \(c\in\{R,G,B,NIR\}\) is the spectral channel, \((x,y)\) is the pixel coordinates, and \(\theta^{+}\), \(\theta^{-}\) are empirically tuned contrast thresholds. The resulting sparse event maps are then smoothed using a Gaussian kernel and stacked alongside the original modalities to generate the final input tensor. This simulation paradigm captures motion-induced or photometric discontinuities that standard frame-based representations may overlook. It particularly enhances the model's ability to detect facial contours and edges in the presence of occlusion or spectral interference. Multispectral and event channels often suffer from misalignment, primarily due to differences in sensor configurations and the asynchronous nature of their data acquisition. To overcome these discrepancies, HUE-Net employs a multi-step registration strategy. First, affine alignment is performed by optimizing cross-spectral mutual information, particularly between the RGB and NIR domains, ensuring a coarse spatial alignment.