## 4 Results
### Self-Trained Analysis
This is due to the great difference in the characteristics of the images acquired with the DigitalPersona sensor compared to those of GreenBit and Dermalog, as is evident from Figure 1. This behavior highlights how the same sensing technology does not always equate to a correspondence between the images' characteristics. In designing the PAD training phase, it is, therefore, necessary to know these characteristics to face a cross-sensor scenario. However, the minutiae-based approach of the FSB partly solves the difficulties of the textural algorithms, proving to be more efficient when trained on DigitalPersona, especially in the cross-scenarios. For this reason, we further investigate this method's behavior through its probability distributions (Figures 10-15). \begin{table}
\begin{tabular}{c c c \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Scanner**} & \multicolumn{3}{c}{**BK 2013**} & \multicolumn{3}{c}{**OR 2019**} \\ \cline{3-8}  & & **APCER (\%)\%** & **BPCER (\%)\%** & **EER** & **APCER (\%)\%** & **BPCER (\%)\%** & **EER** \\ \cline{3-8}  & & **BPCER = 5\%** & **APCER = 5\%** & **BPCER = 5\%** & **APCER = 5\%** & **BPCER = 5\%** & **BPCER (\%)\%** & **EER** \\ \hline \multirow{8}{*}{**BSIF**} & DL & 100.00 & 62.50 & 31.50 & 100.00 & 99.49 & 49.84 \\ \cline{2-8}  & DP & 100.00 & 93.00 & 46.50 & 100.00 & 99.60 & 49.80 \\ \cline{2-8}  & GB & 99.30 & 100.00 & 50.45 & 100.00 & 98.59 & 51.27 \\ \cline{2-8}  & DL+CB & 100.00 & 45.40 & 18.40 & 100.00 & 98.89 & 49.49 \\ \cline{2-8}  & DL+DP & 100.00 & 53.10 & 20.70 & 100.00 & 100.00 & 50.00 \\ \cline{2-8}  & DP+CB & 100.00 & 100.00 & 28.60 & 100.00 & 97.98 & 48.99 \\ \cline{2-8}  & DL+GB+DP & 89.70 & 100.00 & 38.65 & 100.00 & 96.36 & 48.18 \\ \hline \multirow{8}{*}{**LBP**} & DL & 90.80 & 100.00 & 45.55 & 100.00 & 97.68 & 51.83 \\ \cline{2-8}  & DP & 100.00 & 91.90 & 45.90 & 100.00 & 99.80 & 49.90 \\ \cline{2-8}  & GB & 100.00 & 100.00 & 32.95 & 100.00 & 99.49 & 54.36 \\ \cline{2-8}  & DL+CB & 100.00 & 100.00 & 46.30 & 100.00 & 100.00 & 67.99 \\ \cline{2-8}  & DL+DP & 100.00 & 100.00 & 49.50 & 100.00 & 97.78 & 54.94 \\ \cline{2-8}  & DP+CB & 100.00 & 100.00 & 35.25 & 100.00 & 97.07 & 49.09 \\ \cline{2-8}  & DL+GB+DP & 85.20 & 100.00 & 42.45 & 100.00 & 98.99 & 59.95 \\ \hline \multirow{8}{*}{**SpoofBuster**} & DL & 95.20 & 75.50 & 31.15 & 100.00 & 97.58 & 50.18 \\ \cline{2-8}  & DP & 100.00 & 87.20 & 32.25 & 100.00 & 91.41 & 28.26 \\ \cline{2-8}  & GB & 87.60 & 100.00 & 21.65 & 98.90 & 99.70 & 51.09 \\ \cline{2-8}  & DL+CB & 100.00 & 84.50 & 39.55 & 100.00 & 97.17 & 42.72 \\ \cline{2-8}  & DL+DP & 100.00 & 79.60 & 37.45 & 100.00 & 99.60 & 49.01 \\ \cline{2-8}  & DP+CB & 92.70 & 74.70 & 28.05 & 94.03 & 88.69 & 32.62 \\ \cline{2-8}  & DL+GB+DP & 100.00 & 87.30 & 46.60 & 100.00 & 94.65 & 47.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results in terms of accuracy, BPCER, and APCER at different operational points of BSIF, LBP, and SpoofBuster PADs with different single-sensor and multi-sensor training techniques for earlier LivDet editions datasets. Figure 4: Comparison between ROCs of our self-trained PAD systems in an intra- (dashed) and cross- (solid) sensor scenario on the Biometrika dataset from LivDet 2013. Figure 5: Comparison between ROCs of our self-trained PAD systems in an intra- (dashed) and cross- (solid) sensor scenario on the Orcanthus dataset from LivDet 2019. Figure 6: Comparison between ROCs of our self-trained PAD systems in an intra- (dashed) and cross- (solid) sensor scenario on the Dermalog Consensus dataset from LivDet 2021. Figure 7: Comparison between ROCs of our self-trained PAD systems in an intra- (dashed) and cross- (solid) sensor scenario on the Dermalog Screen Spoof dataset from LivDet 2021. Figure 11: Probability distributions of (**a**) real and (**b**) fake fingerprint scores of the GreenBit Screen-Spoof dataset obtained by the PAD Spoofbuster.