# 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  
This section evaluates the effectiveness of S\({}^{2}\)SMA for fine-tuning several pre-trained deep models in handling the face sketch recognition problem. A total of two public datasets were used to evaluate the performances, including XM2 YTS [65] and CUFSF [66]. It should be noted that these datasets have been utilized frequently in the literature [25], [68; 69]. Partition settings of images contained in each database are shown in Table 8. As in earlier research [1; 25], each database was separated into training and testing, as indicated in Table 8. Some example images from XM2 VTS and CUFSF are displayed in Figure 11. It is worth mentioning that the datasets were taken without any image enhancements or pre-processing. S\({}^{2}\)SMA was run 10 times for each dataset with a population size set to 30, and the maximum number of iterations is \(10^{3}\). Figure 10: The convergence curves for large-scale functions F1–F20. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Component** & **Setting** \\ \hline \multirow{6}{*}{Hardware} & CPU & Intel(R) Core (TM) i7-10700 \\  & Frequency & 2.9 GHz \\  & RAM & 16 GB \\  & GPU & Nvidia GeForce GTX 1660 \\  & Super \\  & SSD & 256 GB \\  & Hard Drive & 2 TB \\ \hline \multirow{2}{*}{Software} & Operating System & Windows 10 \\  & Language & MATLAB R2021a \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The details of hardware and software settings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database** &
\begin{tabular}{c} **Number of** \\ **Sketch–Photo Pairs** \\ \end{tabular} & **Training Pairs** & **Testing Pairs** \\ \hline XM2 VTS & 295 & 100 & 195 \\ \hline CUFSF & 1194 & 955 & 239 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The studied database sketches. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function No. ** & **SMA** & **ESMA** & **LSMA** & **AOSMA** & **AOA** \\ \hline
1 & 3.02 \(\times 10^{-11}\) & 3.02 \(\times 10^{-11}\) & 3.02 \(\times 10^{-11}\) & 3.02 \(\times 10^{-11}\) & 3.02 \(\times 10^{-11}\) \\
2 & 3.02 \(\times 10^{-11}\) & 3.02 \(\times 10^{-11}\) & 3.02 \(\times 10^{-11}\) & 3.02 \(\times 10^{The Mean Recognition Rates  
This experiment evaluates the performance of S\({}^{2}\)SMA compared to the original SMA [28], AOA [59], and SMA variants (LSMA [36], AOSMA [37], and ESMA [56]) on face sketch recognition. Table 9 summarizes the mean recognition rate from rank 1 to 10 for XM2 VTS. The results were compared using single-model and multiple-model approaches. The proposed model achieved the highest recognition rate at rank 1 among other algorithms and was non-optimized in all experiments. For example, it outperforms rank 1 with 98.81% in multiple-model tests, while the single-model tests closest to it, FaceNet and ArcFace, scored 85.28%. Table 9 also shows that S\({}^{2}\)SMA reported a 100% accurate recognition rate at rank 2 in multiple-model tests, exceeding other results obtained. The results can be attributed to the efficiency of S\({}^{2}\)SMA in tuning multiple-model features based on their importance and giving weight to each model based on its efficiency. Further study was performed by calculating the cumulative matching characteristic (CMC) generated by S\({}^{2}\)SMA vs. the non-optimized model for FaceNet, ArcFace, VGG-Face, DeepFace, and multiple-model, as given in Figure 12. CMC evaluates face sketch recognition performance along an \(x\)-axis ranging from 1 to \(k\). The \(y\)-axis displays the recognition rate for each rank value. As demonstrated in Figure 12, [PERSON]\({}^{2}\)SMA had a better recognition curve for most ranks between 1 and 10 in all models. Furthermore, as shown in Figure 12, S\({}^{2}\)SMA achieved 100% precision in multiple-model at rank 2. Moreover, at the level of single-model, S\({}^{2}\)SMA at rank 1 outperformed the non-optimized in all models. Figure 11: Sample images from (**a**) XM2 VTS database, and (**b**) CUFSF database. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{