## 4 Experiments
### Analysis of Experimental Results
#### 4.3.2 Signed Fingermark Verification  
Experiments were conducted in the database established in this paper to verify the effectiveness of the proposed SFNet and multi-probability labeling strategy for signed fingermarks. The network was confidently trained with unsigned fingermark data to ensure its ability to detect the liveness of unsigned fingermarks, without any interference from signed noise. SFNet models and an SFNet model incorporating a multi-probability labeling strategy were trained on a comprehensive fingermark dataset containing both signed and unsigned fingermarks (SFNet_1). The test results in Table 4 demonstrate that the signed fingermark liveness detection network has a detection accuracy of over 98%, indicating that SFNet exhibits superior liveness detection performance on both signed and unsigned fingermark datasets. The method that incorporated the multi-probability labeling strategy had higher accuracy on both signed and unsigned fingermark datasets, with 99.00% and 99.26%, respectively. This confirms that the multi-probability labeling strategy significantly improves the accuracy of liveness detection and enhances network performance. In order to verify the existence of signatures interfering with the liveness detection of fingermarks, a set of SFNet models incorporating a multi-probability labeling strategy is trained on the unsigned fingermark training set (SFNet_2). The model's recognition accuracy for the signed fingermark test set is significantly lower than its performance on the unsigned test set, demonstrating a failure to meet the requirements of liveness detection. This confirms that the presence of a signatures has a negative impact on the accuracy of liveness detection, underscoring the need for a signed fingermark database to be established. The original purpose of signed fingermark liveness detection algorithms is to defend against spoofing attacks. Therefore, the ability to resist attacks from fake fingermarks made from different materials is one of the important criteria for a liveness detection method. In this paper, we test the accuracy of a signed fingermark dataset made from different materials, as shown in Table 5. It can be seen that SFNet has good detection results for all five materials in the dataset, which indicates that SFNet has high recognition accuracy for known materials and can withstand spoofing attacks from most known materials. In addition, latex was tested as an unknown material, and compared to the known materials, the number of fake signed fingermarks obtained from latex production is lower, but also has a high recognition accuracy, which indicates that SFNet has a certain degree of robustness to unknown materials. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Train** & **Live** & **Fake** & **Overall** \\ \hline \multirow{2}{*}{SFNet} & Signed & 98.41 & 98.68 & 98.54 \\  & Unsigned & 98.96 & 98.84 & 98.90 \\  & Signed & 98.49 & 99.00 & 98.73 \\  & Unsigned & 98.97 & 99.26 & 99.13 \\  & Signed & 70.62 & 67.96 & 69.35 \\  & Unsigned & 97.98 & 97.79 & 97.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection accuracy results for SFNet and multi-probability label strategy. Unit: %. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Type** & **SFNet** & **SFNet\_1** \\ \hline \multirow{4}{*}{Fake} & Conductive silicone & 98.76 & 99.07 \\  & Skin color silicone & 98.68 & 98.12 \\  & Wood glue & 99.12 & 99.19 \\  & Clear silicone & 98.62 & 98.99 \\  & Fingerprint seals & 99.52 & 99.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance of the network on the signed fingermark database made using different materials. Unit: %.