# Face Spoofing Detection with Stacking Ensembles in Work Time Registration System
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Combination** & **Small** & **Spoofing** & **Large** & **CelebA** & **Rank** \\ \hline B,S,C,N,X,E,V (stacking) & 92.62 \(\pm\) 3.05 & 96.88 \(\pm\) 1.48 & 98.99 \(\pm\) 0.17 & 99.20 \(\pm\) 0.12 & 2.75 \\ \hline S,C,N,X,E (stacking) & 92.15 \(\pm\) 3.36 & 96.85 \(\pm\) 1.51 & 99.00 \(\pm\) 0.17 & 99.19 \(\pm\) 0.10 & 4.25 \\ \hline B,S,C,N,X,E (stacking) & 92.46 \(\pm\) 3.17 & 96.82 \(\pm\) 1.54 & 98.99 \(\pm\) 0.19 & 99.18 \(\pm\) 0.12 & 4.5 \\ \hline B,S,C,N,X (stacking) & 92.63 \(\pm\) 3.04 & 96.88 \(\pm\) 1.53 & 98.95 \(\pm\) 0.20 & 99.12 \(\pm\) 0.13 & 7.25 \\ \hline S,C,N,X,E,V (stacking) & 91.69 \(\pm\) 3.73 & 96.93 \(\pm\) 1.39 & 98.98 \(\pm\) 0.18 & 99.19 \(\pm\) 0.10 & 7.5 \\ \hline B,S,C,N,E,V (stacking) & 92.80 \(\pm\) 3.21 & 96.80 \(\pm\) 1.57 & 98.95 \(\pm\) 0.19 & 99.10 \(\pm\) 0.13 & 9 \\ \hline S,C,N,E,V (stacking) & 92.11 \(\pm\) 4.04 & 96.89 \(\pm\) 1.44 & 98.94 \(\pm\) 0.21 & 99.12 \(\pm\) 0.12 & 10.75 \\ \hline S,C,N,E (stacking) & 92.29 \(\pm\) 3.16 & 96.77 \(\pm\) 1.46 & 98.93 \(\pm\) 0.22 & 99.14 \(\pm\) 0.11 & 11.5 \\ \hline B,S,C,N,X (stacking) & 91.61 \(\pm\) 2.67 & 96.12 \(\pm\) 1.31 & 98.99 \(\pm\) 0.17 & 99.17 \(\pm\) 0.11 & 11.75 \\ \hline B,C,N,X,E,V (stacking) & 92.04 \(\pm\) 3.47 & 96.30 \(\pm\) 1.66 & 98.95 \(\pm\) 0.14 & 99.09 \(\pm\) 0.10 & 13.25 \\ \hline B,S,C,N,X,V (stacking) & 91.43 \(\pm\) 2.91 & 96.07 \(\pm\) 1.33 & 98.99 \(\pm\) 0.17 & 99.17 \(\pm\) 0.09 & 13.75 \\ \hline C,N,X,E,V (stacking) & 91.28 \(\pm\) 4.00 & 96.38 \(\pm\) 1.69 & 98.97 \(\pm\) 0.15 & 99.05 \(\pm\) 0.15 & 16.5 \\ \hline S,C,N,X (stacking) & 90.92 \(\pm\) 2.86 & 95.96 \(\pm\) 1.27 & 99.01 \(\pm\) 0.18 & 99.16 \(\pm\) 0.12 & 17 \\ \hline B,C,N,X,E (stacking) & 91.69 \(\pm\) 3.83 & 96.18 \(\pm\) 1.66 & 98.95 \(\pm\) 0.14 & 99.08 \(\pm\) 0.13 & 17 \\ \hline S,C,N,X,V (stacking) & 90.73 \(\pm\) 3.05 & 95.88 \(\pm\) 1.28 & 98.99 \(\pm\) 0.16 & 99.16 \(\pm\) 0.09 & 20.25 \\ \hline B,C,N,E,V (stacking) & 92.16 \(\pm\) 3.66 & 96.34 \(\pm\) 1.66 & 98.89 \(\pm\) 0.21 & 98.94 \(\pm\) 0.18 & 20.5 \\ \hline B,S,C,N,V (stacking) & 91.48 \(\pm\) 2.72 & 95.91 \(\pm\) 1.39 & 98.94 \(\pm\) 0.20 & 99.09 \(\pm\) 0.13 & 21.25 \\ \hline B,S,C,N (stacking) & 91.29 \(\pm\) 2.72 & 95.86 \(\pm\) 1.34 & 98.95 \(\pm\) 0.21 & 99.11 \(\pm\) 0.12 & 22 \\ \hline B,C,N,E (stacking) & 91.85 \(\pm\) 4.07 & 96.18 \(\pm\) 1.65 & 98.91 \(\pm\) 0.21 & 98.93 \(\pm\) 0.17 & 23 \\ \hline C,N,X,E (stacking) & 90.61 \(\pm\) 3.80 & 96.25 \(\pm\) 1.80 & 98.95 \(\pm\) 0.16 & 99.05 \(\pm\) 0.11 & 23.25 \\ \hline S,C,N,V (stacking) & 90.73 \(\pm\) 3.06 & 95.93 \(\pm\) 1.38 & 98.94 \(\pm\) 0.22 & 99.07 \(\pm\) 0.13 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: All obtained accuracies [%] for different stacking and voting ensemble combinations over five runs of a 5-fold cross-validation, part 1. The rank was obtained by averaging the position for all four datasets, for each combination. The models used are Bezel (B), Smartphone Detection CNN (S), Context (C), Image Analysis CNN (N), EfficientNetV2 (E), ConvNeXt (X), and Vision Transformer (V).