## 1 Introduction  
The use of face recognition is a primary feature of artificial intelligence in the contemporary world, with applications ranging from border security and surveillance to smartphone unlocking and even humanitarian aid [1]. A growing body of work within deep learning has enabled face recognition models to achieve nearly human accuracy in highly controlledsettings [2], yet these models tend to perform poorly in realistic settings [3]. More specifically, occlusions [4], illumination changes [5], aging [6], trauma [7], low resolution [8], and environmental artifacts such as fog or motion blur [9] pose significant challenges. In such scenarios, traditional RGB recognition systems fail to generalize because of their narrow predictive horizons and inability to measure uncertainty in their decision-making processes. In addition, face recognition systems are mostly built to operate as contemporary models, which means that they lack flexibility in reasoning. While offering high accuracy on standard benchmarks, they lack confidence estimation mechanisms and do not adapt to variable input quality [10]. This shortfall is critical in humanitarian applications, such as post-disaster victim identification or forensic analysis, where incorrect predictions can have severe consequences. Facial input is degraded by injury, partial occlusion, or poor lighting [11]. Furthermore, due to the computational footprint of most state-of-the-art models, they become infeasible to deploy on edge devices in resource-constrained environments where high-stakes, fast decision-making with minimal power usage is important [12]. Recent studies have explored different aspects of robust facial analysis under difficult imaging conditions. For instance, HyperFace [13] presents a deep fusion model for hyperspectral face recognition using high-dimensional spectral data, while MS-EVS [14] uses asynchronous multispectral event data for face detection in dynamic scenes. These works highlight the potential of spectral and temporal cues, yet they often lack uncertainty estimation or are limited to detection tasks. In parallel, probabilistic deep models such as BPMB [15] and RobustFace [16] have introduced Bayesian or ensemble-based calibration strategies, but these are typically computationally expensive or single-modality. HUE-Net is motivated by the need to unify these advances into a single, lightweight, multi-modal recognition system that is both uncertainty-aware and edge-deployable. To address these issues, we developed HUE-Net: a Human-centric, Uncertainty-aware, Event-fused Network tailored for face recognition in adverse conditions. HUE-Net is built upon three guiding principles. First, it incorporates Cross-Spectrum Perception, integrating visible (RGB), near-infrared (NIR), and event-based representations to enhance robustness under a wide variety of lighting and motion conditions. Second, it incorporates uncertainty modeling through a variational multi-branch structure, empowering the network with the ability to not just make predictions but also evaluate the confidence behind those predictions. Third, HUE-Net is crafted to be resource-efficient, blending convolutional and transformer-based modules equipped with spectral attention and low-rank transformations for real-time inference on edge devices. The factor that distinguishes HUE-Net from its predecessors is the unified cooperation of perception, reasoning, and adaptability. Unlike the rest of the models, which utilize multispectral inputs or probabilistic outputs separately, HUE-Net integrates all of these into one architecture, which selectively differentiates attention to the most informative spectral channels and predicts uncertainty while still performing under hardware constraints. This enables HUE-Net to be one of the strongest contenders for state-of-the-art recognition accuracy and a reliable solution for mission-critical tasks in uncontrolled and ethically sensitive environments. In this work, we focus on the architectural design and empirical validation of HUE-Net.