## 4 Experiments
### Evaluation Metrics  
To evaluate the performance of HUE-Net, we systematically used a set of quantitative metrics that measure recognition accuracy, calibration accuracy, and robustness to modality degradation. These criteria were selected to encompass traditional face recognition evaluation as well as the distinctive features of HUE-Net's implementation, especially its probabilistic modeling and multispectral versatility. As the primary metric for classification, we present top-1 identification accuracy. This is defined as  
\[Acc_{top-1}=\frac{1}{N}\sum_{i=1}^{N}1(\hat{y}_{i}=y_{i}) \tag{20}\]  
where \(N\) stands for the total number of test samples, \(y_{i}\) represents the actual identity label, \(\hat{y}_{i}\) symbolizes the predicted label, and \(1(\cdot)\) is the indicator function that returns 1 when a particular condition is satisfied. Furthermore, we assessed the Rank-1 Verification Rate \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **Subjects** & **Images** & **Modalities** & **Resolution** & **Conditions Present** & **Used For** \\ \hline IJB-C [17] & 3531 & 31,334 & RGB & Varying & \begin{tabular}{l} Occlusion, Pose, Blur, \\ Low Light \\ \end{tabular} & Validation/Testing \\ \hline N-SpectralFace [14] & 1200 & 28,000 & \
\begin{tabular}{l} RGB, NIR, \\ Event (sim) \\ \end{tabular} & 224 \(\times\) 224 &
\begin{tabular}{l} Motion Blur, Low \\ Light, Alignment Shift \\ \end{tabular} & Training/Testing \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of datasets used in experiments. (\(VR_{FAR}\)) on IJB-C [17], especially under a fixed false acceptance rate (\(FAR\)), as performed in open-set recognition system reset protocols. This metric, as with other metrics of performance, is derived from the ROC curve and is calculated as  
\[VR_{FAR}=max\{TPR|FPR\leq\tau\} \tag{21}\]  
where \(TPR\) and \(FPR\) denote the true positive rate and false positive rate, respectively, and \(\tau\) is the \(FAR\) threshold. ROC AUC (Receiver Operating Characteristic--Area Under Curve) is used to summarize verification performance over all operating points. In this case, ROC AUC is defined as the integral of the PR-FPR curve:  
\[AUC=\int_{0}^{1}TPR(FPR)d(FPR) \tag{22}\]  
This metric captures the likelihood that a randomly selected positive pair (identical identity) is scored higher than a randomly selected negative pair (different identity). To assess how well the model predicted confidence matches with the actual likelihood of being correct, we calculate Expected Calibration Error (ECE). Given a set of predictions with confidence values, ECE quantifies the weighted average accuracy and confidence disparity across \(M\) bins:  
\[CE=\sum_{m=1}^{M}\frac{\left|B_{m}\right|}{N}|acc(B_{m})-conf(B_{m})| \tag{23}\]  
Here, \(B_{m}\) is the set of samples falling into the \(m\)-th confidence bin, \(acc(B_{m})\) is the empirical accuracy in that bin, and \(conf(B_{m})\) is the mean predicted confidence. Lower ECE values indicate better-calibrated predictions. The _Brier_ score offers a complementary view of probabilistic accuracy, especially for softmax outputs. It measures the mean squared error between the predicted probability \(\hat{p}_{i}\) and true class indicator \(y_{i}\in\{0,1\}\):  
\[Brier=\frac{1}{N}{\sum_{i=1}^{N}}(\hat{p}_{i}-y_{i})^{2} \tag{24}\]  
Smaller values show both confident and correct predictions; thus, both overconfidence and underconfidence are punished. To quantify the robustness under partial spectral loss, we propose a new metric, the Spectral Robustness Score (\(SRS\)), which assesses accuracy degradation when one or more modalities are blocked during inference. \(Acc_{full}\) denotes the accuracy using all modalities, and \(Acc_{masked}^{(c)}\) is the accuracy with channel ccc dropped.