## 5 Discussion  
This section compares biometric authentication methods evaluated on the NUPT-FPV dataset (Table 12). Prior studies reported varying multimodal accuracies, such as 88.05% for FPV-Net [46], 93.51% for LSTM with image augmentation [37], and 95.41% for FPV-CSAFM [48]. STMPFPV-Net [20] achieved a high FP accuracy of 99.26% but showed weaker FV performance at 75.99%. Recent approaches like NLNet [47] and DenseNet [35] reported 99.10% and 97.00%, respectively. The proposed MobileNet-based system outperformed all the previously reported methods on the NUPT-FPV dataset. It achieved unimodal accuracies of 97.47% for fingerprint and 97.54% for finger vein, exceeding prior results, such as FPV-CSAFM (91.58%/86.96%) and LSTM with augmentation (82.87%/79.10%). The proposed MobileNet-based system outperformed all the previous methods, achieving unimodal accuracies of 97.47% (FP) and 97.54% (FV), and multimodal accuracies of 99.37% (score-level), 99.58% (hybrid-level), and 99.79% (hybrid fusion with 0.7 score/0.3 feature). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Method** & **FP Unimodal Accuracy** & \
\begin{tabular}{c} **FV Unimodal Accuracy** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Multimodal Accuracy** \\ **(FPV)** \\ \end{tabular} \\ \hline
[46] & FPV-Net & 77.44\% & 76.43\% & 88.05\% \\
[37] & Long Short-Term Memory (LSTM) & _Without Aug._: 80.09\% & _Without Aug._: 75.99\% & _Without Aug._: 90.33\% \\  & FPV-CSAFM & & _With Aug._: 79.10\% & _With Aug._: 93.51\% \\
[48] & (Channel Spatial Attention Fusion) & 91.58\% & 86.96\% & 95.41\% \\
[20] & STMPFPV-Net & 99.26\% & 75.99\% & 96.21\% \\
[47] & NLNet & Not used & Not used & 99.10\% \\  & Deep Cancelable & & & & \\
[51] & Multibiometric Authentication using NMF & 95.31\% & 95.31\% & 95.31\% \\
[35] & DenseNet & Not used & Not used & 97.00\% \\
[51] & Fusion-based Information Bottleneck & Not used & Not used & 90.71\% \\  & & & & Score-level: 99.37\% \\
**Our system** & **MobileNet + Fusion Strategies** & 97.47\% & 97.54\% & Hybrid-level: 99.58\% \\  & & & & Hybrid (0.7 score, 0.3 feature): 99.79\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of the proposed and existing methods on the NUPT-FPV dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** &
\begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular} & **FAR** & **FRR** & **EER** \\ \hline SVM & 0.8586 & 0.8716 & 0.8586 & 0.8556 & 0.1174 & 0.1619 & 0.1396 \\ Decision Tree & 0.7172 & 0.7309 & 0.7172 & 0.7135 & 0.2759 & 0.3008 & 0.2883 \\ Random Forest & 0.9725 & 0.9758 & 0.9725 & 0.9721 & 0.0246 & 0.0328 & 0.0287 \\ KNN & 0.5682 & 0.7436 & 0.5682 & 0.5805 & 0.2518 & 0.4435 & 0.3477 \\ Naive Bayes & 0.3649 & 0.5499 & 0.3649 & 0.3735 & 0.3200 & 0.6858 & 0.5029 \\ Proposed system & 0.9979 & 0.9981 & 0.9979 & 0.9979 & 0.0016 & 0.0021 & 0.0018 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance comparison of classical ML models. Notably, the hybrid fusion model with a dominant score-level weight (0.7 score/0.3 feature) achieved the highest accuracy of 99.79% using a lightweight MobileNet backbone on the NUPT-FPV dataset. This emphasizes its strong suitability for real-world biometric applications, especially in resource-constrained environments--an aspect seldom addressed in previous studies--and it outperforms the previously reported methods for this task [37; 46; 47]. These results not only establish a new technical benchmark but also demonstrate the feasibility and practical value of deploying high-accuracy biometric systems in practical scenarios, setting this work apart from earlier efforts in the field. These improvements are attributed to MobileNet's efficient feature extraction and the complementary strengths of the employed fusion strategies, confirming the robustness and superiority of the proposed system. We recognize the potential for overfitting given the high accuracy achieved. To address this issue, several techniques can be employed.