## 4 Results
### Self-Trained Analysis
\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}  & & **BPCER** & **APCER** & **Accuracy** & **BPCER** & **APCER** & **Accuracy** \\ \hline \multirow{8}{*}{**BSIF**} & DL & 71.80 & 0.10 & 64.05 & 99.90 & 0.00 & 52.41 \\ \cline{2-8}  & DP & 98.00 & 0.00 & 51.00 & 99.90 & 0.00 & 52.41 \\ \cline{2-8}  & GB & 3.10 & 98.20 & 49.35 & 96.67 & 4.41 & 51.64 \\ \cline{2-8}  & DL+GB & 33.00 & 3.20 & 81.90 & 99.70 & 0.00 & 52.50 \\ \cline{2-8}  & DL+DP & 49.30 & 1.30 & 74.70 & 100.00 & 0.00 & 52.36 \\ \cline{2-8}  & DP+GB & 7.30 & 72.30 & 60.20 & 99.29 & 0.00 & 52.69 \\ \cline{2-8}  & DL+GB+DP & 0.50 & 92.70 & 53.40 & 99.90 & 0.00 & 52.41 \\ \hline \multirow{8}{*}{**LBP**} & DL & 0.00 & 95.00 & 52.50 & 96.57 & 2.11 & 52.89 \\ \cline{2-8}  & DP & 92.50 & 0.70 & 53.40 & 99.80 & 0.00 & 52.45 \\ \cline{2-8}  & GB & 30.50 & 34.10 & 67.70 & 99.09 & 1.19 & 52.17 \\ \cline{2-8}  & DL+GB & 0.00 & 96.30 & 51.85 & 89.49 & 29.50 & 41.92 \\ \cline{2-8}  & DL+DP & 0.00 & 99.60 & 50.20 & 91.41 & 8.36 & 52.07 \\ \cline{2-8}  & DP+GB & 43.20 & 31.50 & 62.65 & 100.00 & 0.00 & 52.36 \\ \cline{2-8}  & DL+GB+DP & 0.30 & 89.00 & 55.35 & 95.25 & 5.24 & 51.88 \\ \hline \multirow{8}{*}{**SpoofBuster**} & DL & 74.50 & 1.10 & 62.20 & 83.23 & 18.84 & 50.48 \\ \cline{2-8}  & DP & 99.70 & 0.00 & 50.15 & 93.93 & 0.37 & 55.05 \\ \cline{2-8}  & GB & 0.20 & 96.70 & 51.55 & 80.60 & 20.86 & 50.67 \\ \cline{2-8}  & DL+GB & 80.60 & 1.50 & 58.95 & 94.14 & 2.48 & 53.85 \\ \cline{2-8}  & DL+DP & 94.00 & 0.00 & 53.00 & 99.80 & 0.28 & 52.31 \\ \cline{2-8}  & DP+GB & 42.20 & 13.50 & 72.15 & 93.84 & 0.46 & 55.05 \\ \cline{2-8}  & DL+GB+DP & 96.70 & 0.10 & 51.60 & 98.38 & 0.18 & 53.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results in terms of accuracy, BPCER, and APCER with a threshold at 0.5 of BSIF, LBP, and SpoofBuster PADs with different single-sensor and multi-sensor training techniques for earlier LivDet editions datasets. Figure 3: Comparison of self-trained protocol accuracies for (**a**) BSIF, (**b**) LBP, and (**c**) SpoofBuster with different single-sensor and multi-sensor training techniques. training and testing on data obtained from the same sensor and acquisition method, the use of additional data increases the error. Conversely, if the system is to be used in critical applications and the priority is to avoid rejecting genuine users, adding various sensors in the training set improves the spoof detection. This is especially evident when PADs are assaulted with never-seen-before attacks in the cross-method scenario, but it is also true in the intra-sensor and intra-method scenarios. For example, note the behavior of the PAD SpoofBuster when tested on consensual GreenBit data (GB CC); training on GreenBit data alone leads to an APCER at 5% and a BPCER equal to 9.88%; adding Dermalog data during the training phase drops the error to 6.41%. About the same improvement is obtained by adding DigitalPersona data. ROC curves shown in Figures 4-9 give us more insights about these scenarios. They highlight that for the BSIF detector, training on GreenBit, Dermalog, and DigitalPersona is optimal at various operational points; for the SpoofBuster detector, the most effective combination is given by training on GreenBit and DigitalPersona. It is also easy to see how the training can sometimes be ineffective; for instance, a BSIF-based PAD cannot distinguish the images coming from the Orcanthus sensor (Figure 5), and the LBP-based detector even reciprocates the classes, namely, it predicts a negative class as a positive class and vice versa, since the ROC is u-shaped. This case is frequent when DigitalPersona is used as the only dataset for training in combination with hand-crafted features.