## 2 Systematic Literature Review (SLR)
### SLR Discussion
To address these concerns and enhance system adaptability, we employed score-level fusion, which integrates the outputs of independent FP and FV classifiers. This dual strategy both increases recognition accuracy and contributes to greater flexibility and robustness under varying operational conditions [33, 35]. A distinguishing aspect of our study is the deployment of a comprehensive modern dataset that captures real-world variabilities and supports robust benchmarking of multimodal biometric systems. The use of large and diverse datasets is critical given that the prior research often suffers from limited sample diversity or scope, thus constraining the generalizability and reliability of the results [10, 12]. For instance, as summarized in Table 1, the SDUMLA-HMT dataset was the most frequently used, cited in seven studies for its multimodal content. The FVC series appeared in four fingerprint-based studies, while the CASIA and PolyU Palm Vein datasets supported investigations involving iris and vein modalities. Our selection of the NUPT-FPV dataset, which includes both FPs and FVs for the same subjects, enables more realistic multimodal evaluation and has only recently begun to appear in the contemporary literature, as seen in [PERSON] et al. [20] and [PERSON] et al. [21]. Our experimental results demonstrate that hybrid multimodal systems that integrate FP and FV modalities and employ advanced fusion strategies exhibit a significant enhancement in accuracy compared to their unimodal counterparts. This improvement aligns with the broader trends in the literature, where the combination of physiological traits at multiple fusion levels has consistently demonstrated superior performance in terms of security and generalizability [10, 28, 33, 35]. As further shown in Table 3, feature-level fusion was the most frequently adopted strategy, appearing in twenty-five studies, followed by score-level fusion in sixteen studies. Fewer works applied decision-level fusion (six studies) or explored both feature- and score-level fusion together (five studies). This distribution highlights the prominence of early- and mid-level integration techniques in the current multimodal biometric research. These results confirm that hybrid fusion frameworks can be effectively scaled when evaluated using real-world heterogeneous data, demonstrating their significant potential for implementation in modern biometric applications [12, 35]. In summary, our research reinforces the advantages of integrating FP and FV modalities within a combined fusion framework. This approach has been shown to enhance the robustness and accuracy of biometric identification systems, outperforming unimodal systems in terms of recognition rate and resistance to spoofing or forgery attempts [10, 33, 35]. Furthermore, this study highlights the importance of the current large-scale datasets for the meaningful evaluation and advancement of biometric technologies. Comprehensive datasets facilitate more realistic testing scenarios and contribute to the development of generalizable high-performing models [10, 35]. Promising future research directions include expanding the multimodal framework to incorporate additional biometric traits, such as face, iris, palm vein, and behavioral characteristics, to further improve system robustness and user convenience [12, 33, 35]. In addition, exploiting advanced DL-based fusion techniques offers significant potential for optimizing both security and usability. These approaches can exploit complex relationships within and across biometric modalities, enabling systems to better adapt to diverse usage environments and threats [1, 12].