## 2 Systematic Literature Review (SLR)
### SLR Discussion  
Multimodal biometric systems have garnered increasing recognition for their capacity to mitigate the limitations inherent in unimodal approaches, particularly in real-world environments characterized by sensor noise, intraclass variability, and sophisticated spoofing attempts. By integrating information from multiple biometric traits, these systems achieve significantly enhanced accuracy, robustness, and resilience against fraudulent activities compared to systems that rely on a single modality [10; 33; 35]. In our study, we opted to integrate FP and FV modalities, a decision informed by their complementary characteristics. FPs are distinguished by their high discriminatory power; however, they are susceptible to surface-level issues such as skin conditions or superficial damage. In contrast, FV patterns located beneath the skin are significantly more challenging to counterfeit and are less affected by surface noise, thereby offering a layer of security and reliability that addresses the limitations of FP-based systems. Despite these advantages, FV biometrics present several practical limitations that can affect system performance, particularly in real-world conditions. FV recognition requires near-infrared imaging devices and is sensitive to variations in lighting and finger positioning during acquisition. Additionally, dry or damaged skin and low-contrast or noisy vein patterns--caused by physiological factors such as poor blood circulation or cold extremities--can reduce image clarity and impact the reliability of feature extraction [20; 35; 46]. These challenges highlight the importance of robust preprocessing and fusion mechanisms when integrating FV in multimodal systems. The fusion of FP and FV traits facilitates the development of robust biometric recognition systems capable of mitigating the weaknesses inherent in each modality [12; 35]. The robustness and security of biometric authentication systems are of paramount importance, particularly in the face of spoofing attempts and environmental noise. Our results show that multimodal fusion strengthens resistance to these challenges, aligning with broader strategies in the field. For example, advanced defense mechanisms such as reversible robust watermarking techniques--like those described in [20] for combating geometric attacks--offer valuable perspectives on designing systems that maintain integrity under various threats. Drawing upon these approaches, our hybrid fusion model not only improves recognition performance but also bolsters the system's resilience against potential adversarial actions and environmental disturbances, indicating a promising direction for future enhancement of multimodal biometric security [35; 47]. To fully leverage the advantages of both modalities, our approach employed a hybrid fusion strategy that integrates feature- and score-level fusion. Feature-level fusion combines the unique attributes of FP and FV in the early processing stage, resulting in a comprehensive high-dimensional representation that enhances the overall discriminative capability \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Method**} & **Number of Studies** & **References** & **Reported Results** \\ \hline ML & 24 & [2; 6; 7; 10; 12; 13; 14; 16; 17; 18; 19; 22; 26; 28; 30; 31; 33; 35; 36; 38] & Up to 100\% (Feature-level, t-norm), 99.33\% (feature-level), 98.45\% (decision-level) \\ DL & 23 & [1; 2; 4; 6; 8; 9; 11; 14; 15; 20; 21; 25; 27; 29; 32; 34; 36; 38; 40; 41; 43; 45] & 100\% (score-level, CNN), 99.9\% (feature-level, CNN), 98.01\% (CNN) + (CNN) \\ ML + DL & 3 & [2; 14; 25] & Up to 99.6\% (feature-level, SVM + PatternNet), 98.99\% (score-level, SVM + FaceNet) \\ \hline \end{tabular}
\end{table} 
Table 4: Methodological approaches across related studies. of the system [10, 33]. Nevertheless, feature-level fusion may encounter difficulties due to incompatibilities between feature sets or an increase in the dimensionality of the feature space [12, 28].