# Fine-Tuning of Pre-Trained Deep Face Sketch Models Using Smart Switching Slime Mold Algorithm
## 4 Experimental Results and Analysis
### Evaluation on Face Sketch Recognition
\begin{tabular}{c} **Deep-Face** \\ **Model** \\ \end{tabular}  } & \multirow{2}{*}{**Algorithm**} & \multicolumn{8}{c}{**Accuracy (\%)**} \\ \cline{3-13}  & & **Rank** & **Rank** & **Rank** & **Rank** & **Rank** & **Rank** & **Rank** & **Rank** & **Rank** & **Rank** & **Rank** \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \multirow{6}{*}{Facenet} & S\({}^{2}\)SMA (proposed) & **85.28** & 91.28 & 94.56 & 96.15 & 97.08 & 97.49 & 98.21 & 98.72 & 98.92 & 99.13 \\ \cline{2-13}  & SMA [28] & 83.79 & 91.13 & 94.26 & 95.85 & 96.82 & 97.69 & 97.95 & 98.26 & 98.46 & 98.67 \\ \cline{2-13}  & ESMA [56] & 84.67 & 90.67 & 94.00 & 95.74 & 96.92 & 97.38 & 97.95 & 98.15 & 98.36 & 98.46 \\ \cline{2-13}  & LSMA [36] & 84.46 & 91.08 & 94.82 & 96.36 & 97.38 & 98.00 & 98.46 & 98.82 & 99.03 & 99.23 \\ \cline{2-13}  & AOSMA [37] & 83.79 & 90.77 & 94.26 & 95.79 & 96.87 & 97.49 & 97.85 & 98.10 & 98.51 & 98.77 \\ \cline{2-13}  & AOA [59] & 83.38 & 91.33 & 94.00 & 95.49 & 96.56 & 97.18 & 97.44 & 97.95 & 98.10 & 98.56 \\ \cline{2-13}  & Non-optimized & 84.62 & 90.77 & 93.85 & 95.90 & 96.92 & 97.95 & 98.46 & 98.46 & 98.46 & 98.46 & 98.46 \\ \hline \multirow{6}{*}{ArcFace} & S\({}^{2}\)SMA (proposed) & **85.28** & 93.13 & 97.03 & 97.95 & 98.67 & 99.13 & 99.33 & 99.49 & 99.49 & 99.59 \\ \cline{2-13}  & SMA & 84.72 & 93.18 & 96.36 & 96.92 & 98.00 & 98.36 & 98.67 & 98.87 & 99.28 & 99.38 \\ \cline{2-13}  & ESMA & 84.87 & 93.18 & 96.51 & 97.28 & 98.10 & 98.51 & 98.82 & 99.03 & 99.33 & 99.44 \\ \cline{2-13}  & LSMA & 84.36 & 92.31 & 96.26 & 97.54 & 98.26 & 98.46 & 99.03 & 99.13 & 99.18 & 99.23 \\ \cline{2-13}  & AOSMA & 84.15 & 92.10 & 96.05 & 97.54 & 97.95 & 98.51 & 99.03 & 99.18 & 99.18 & 99.44 \\ \cline{2-13}  & AOA & 83.28 & 92.92 & 95.74 & 97.33 & 98.10 & 98.67 & 98.82 & 98.97 & 99.13 & 99.18 \\ \cline{2-13}  & Non-optimized & 84.10 & 93.33 & 97.44 & 97.44 & 98.46 & 98.46 & 98.46 & 98.97 & 99.49 Figure 12: Cont. Fitness Value Analysis  
This section aims to compare the performance of \(S^{2}\)SMA compared to the original SMA [28], AOA [59], and SMA variants (LSMA [36], AOSMA [37], and ESMA [56]). Table 10 includes the results of the single-model and multiple-model approaches. Table 10 shows the best, worst, average, and mean of all algorithms, as well as the standard deviation. \(S^{2}\)SMA received the highest mean value for both single-model and multiple-model approaches out of all the other algorithms. For example, the best mean value for a multiple-model is \(S^{2}\)SMA, with a value of \(-2.34\)\(\times\)\(10^{-1}\). On the other hand, the worst mean value for the multiple-model approach is ESMA, with a value of \(-1.77\)\(\times\)\(10^{-1}\), the lowest among all algorithms. Figure 12: The CMC results of \(S^{2}\)SMA as compared with non-optimized for (**a**) FaceNet, (**b**) ArcFace, (**c**) VGG-Face, (**d**) DeepFace, and (**e**) multiple-model. Convergence Evaluation  
Further investigation was done by analysing the convergence graphs between S\({}^{2}\)SMA and other algorithms used in Table 10 and plotted in Figure 13. As shown in Figure 13, S\({}^{2}\)SMA has the fastest and most effective convergence speed, outperforming all other optimizers for single-model and multiple-model face sketch recognition. This demonstrates that S\({}^{2}\)SMA is capable of addressing large-scale problems. In other words, the ability of all single-model and multiple-model approaches to converge has been greatly improved by tuning features and similarity and by combining all deep-face models. Statistical Analysis  
To statistically evaluate the performance of S\({}^{2}\)SMA compared to other algorithms, the \(p\)-value of the Wilcoxon signed-rank test [67] is computed. As shown in Table 11, S\({}^{2}\)SMA is significantly better than other algorithms on all test problems. This shows that the original SMA's capability for exploration and exploitation has been significantly enhanced by the addition of embedded rules and operations.