## 2 Systematic Literature Review (SLR)
### SLR Result and Findings
#### 2.2.1 Dataset  
The recent advancements in biometric authentication research have been driven significantly by the availability, diversity, and richness of public datasets. These datasets play a critical role in enabling researchers to design, develop, and evaluate multimodal biometric systems. Among them, the Shandong University Multimodal Laboratory Human-Model Test (SDUMDA-HMT) dataset stands out for its comprehensive collection of biometric modalities, including the face, iris, finger vein (FV), fingerprint (FP), and gait. This dataset has been adopted in numerous studies, providing a robust foundation for multimodal experiments and supporting the exploration of innovative fusion strategies [6; 7; 8; 9; 10; 11; 12].  
In addition to SDUMDA-HMT, several public datasets have made substantial contributions to the field. The CASIA dataset is recognized for offering iris, palm, and finger vein modalities and has been used in at least three multimodal studies [12; 13]. The NUPT-FPV dataset, although cited less frequently, remains highly relevant for studies focused specifically on fingerprint and finger vein fusion as it provides paired data for these modalities. For fingerprint recognition, the Fingerprint Verification Competition (FVC) datasets from 2000 to 2004 are among the most utilized, referenced in four studies exploring unimodal and multimodal approaches [14; 15; 16; 17]. Additional datasets such as PolyU Palm Vein and IITD Palm Print have also been cited, although each in only a single study, reflecting their specialized but valuable roles in evaluating specific modalities [12; 18].  
Moreover, while many studies rely heavily on well-established public datasets, some research efforts involve collecting proprietary or self-recorded datasets to address specific experimental needs, further enriching the field's methodological diversity. Table 1 presents a consolidated overview of the key public datasets, summarizing their frequency of use, biometric modalities, and corresponding references.  
For instance, the SDUMDA-HMT dataset was cited in seven studies and supported five different modalities, making it ideal for evaluating advanced multimodal systems. In comparison, the FVC datasets were used in four studies that focused exclusively on fingerprint recognition. CASIA appeared in three studies and provided a balanced distribution of iris, palm, and finger vein modalities. Meanwhile, NUPT-FPV offered two-modality data (FP and FV) and was used in two notable works. Specialized datasets such as PolyU Palm Vein and IITD Palm Print were each cited in one study and are typically used to test specific biometric traits.  
Figure 2: The number of biometric modalities, datasets, fusion strategies, and computational approaches.  
The diversity and accessibility of these datasets allow researchers to conduct experiments under varied and challenging conditions, which improves the robustness, accuracy, and generalizability of biometric authentication systems. Leveraging both public and self-recorded datasets enables the development of resilient models capable of addressing real-world challenges. As the field of biometric authentication research continues to evolve, the evolution and continued use of such datasets remains fundamental to advancing multimodal biometric authentication research [6; 12; 17].