## 4 Results
### Multimodal-Based DL Experiments
#### 4.2.1 Impact of Fusion Weights  
In addition to evaluating the hybrid fusion strategy's performance, we systematically analyzed how adjusting the relative weights of feature-level and score-level components impacts system performance. Using the MobileNet model, we tested multiple hybrid configurations, including balanced (0.5:0.5) and unbalanced weight distributions (e.g., 0.3:0.7 and 0.7:0.3). As shown in Table 9, the balanced configuration achieved consistently high metrics: accuracy (0.9958), precision (0.9963), recall (0.9958), and F1-score (0.9958), alongside low error rates (FAR = 0.0039, FRR = 0.0040, and EER = 0.0039). However, the optimal performance occurred with a 0.7 (score) to 0.3 (feature) weighting, yielding the highest accuracy (0.9979) and precision (0.9981), as well as the lowest error rates (FAR = 0.0016, FRR = 0.0021, and EER = 0.0018). These results demonstrate that fine-tuning fusion weights in the hybrid architecture enhances adaptability to diverse biometric scenarios and improves overall system reliability.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Pretrained** & **Evaluation** & **Feature-Level** & **Score-Level** & **Hybrid-Level** \\
**CNN Model** & **Metric** & **Fusion** & **Fusion** & **Fusion** \\ \hline \multirow{8}{*}{MobileNet} & Accuracy & 0.9705 & 0.9937 & 0.9958 \\  & Precision & 0.9746 & 0.9948 & 0.9963 \\ \cline{1-1}  & Recall & 0.9705 & 0.9937 & 0.9958 \\ \cline{1-1}  & F1-Score & 0.9692 & 0.9929 & 0.9958 \\ \cline{1-1}  & FAR & 0.0254 & 0.0062 & 0.0039 \\ \cline{1-1}  & FRR & 0.0391 & 0.0127 & 0.0040 \\ \cline{1-1}  & EER & 0.0323 & 0.0094 & 0.0039 \\ \hline \end{tabular}
\end{table}
Table 8: Performance comparison of the proposed multimodal biometric system.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Pretrained** & **Evaluation Metrics** & **Hybrid-Level Fusion** & **Hybrid-Level Fusion** & **Hybrid-Level Fusion** \\
**CNN Model** & **(0.5 Score, 0.5 Feature)** & **(0.3 Score, 0.7 Feature)** & **(0.7 Score, 0.3 Feature)** \\ \hline \multirow{8}{*}{MobileNet} & Accuracy & 0.9958 & 0.9662 & 0.9979 \\  & Precision & 0.9963 & 0.9736 & 0.9981 \\ \cline{1-1}  & Recall & 0.9958 & 0.9662 & 0.9979 \\ \cline{1-1}  & F1-Score & 0.9958 & 0.9662 & 0.9979 \\ \cline{1-1}  & FAR & 0.0039 & 0.0234 & 0.0016 \\ \cline{1-1}  & FRR & 0.0040 & 0.0321 & 0.0021 \\ \cline{1-1}  & EER & 0.0039 & 0.0278 & 0.0018 \\ \hline \end{tabular}
\end{table}
Table 9: Impact of feature/score weighting on hybrid fusion performance.  
The confusion matrix in Figure 7 shows a clear diagonal dominance, indicating that the model accurately distinguishes between classes. The low off-diagonal values reflect minimal misclassification, demonstrating the robustness and high discriminative power of the proposed fusion strategy.  
As seen in Figure 8, the ROC curves of all the classes are tightly clustered near the top-left corner, yielding AUC values close to 1.0. This confirms the model's excellent ability to differentiate between genuine and impostor classes in a multi-class biometric setting.  
Figure 8: ROC curve illustrating the performance for class 001 using the hybrid-level fusion strategy (70% score + 30% feature) based on the MobileNet architecture.  
Figure 7: Part of the confusion matrix of the hybrid-level fusion model (70% score + 30% feature) using MobileNet.