# A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance
## 5 Experimentation and Results
### Images' Utility Maintenance
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Loss Rate** & **Angry** & **Fear** & **Happy** & **Sad** & **Surprise** & **Neutral** \\ \hline QM-VAE & 0.167 & 0.4 & 0.064 & 0.171 & 0.33 & 0.117 \\ Blindfold & 0.396 & 0.701 & 0.039 & 0.726 & 0.75 & 0.870 \\ Mosaic & 0.226 & 0.290 & 0.196 & 0.149 & 0.208 & 0.232 \\ Laplace & 0.321 & 0.204 & 0.178 & 0.246 & 0.333 & 0.191 \\ Cartoon & 0.755 & 0.452 & 0.504 & 0.343 & 0.542 & 0.423 \\ AMT-GAN & 0.551 & 0.776 & 0.144 & 0.677 & 0.826 & 0.476 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Facial expression loss rate under different methods. Figure 6: The changing trend of the expression loss rate. Figure 7: The result of service quality maintenance (**a**) Image utility was maintained without expression loss. (**b**) Image utility was maintained only after expression loss was added. (**c**) Image utility was damaged even after adding expression loss. Figure 8: Comparison of the resulting images of QM-VAE and AMT-GAN. Figure 10: The error bar diagrams of de-identified image by different methods. Figure 9: The scatter distribution diagram of the difference between the result values of facial expression recognition before and after processing by different methods. We can find that if we do not consider the MSE at all, the resulting image will almost collapse. We designed experiments to compare the expression loss of images generated with and without considering the loss function. The experimental data are shown in Table 4.  
The experimental results reveal that the effect of the loss function without the MSE is obviously different from that with the MSE. Within a certain range, the MSE plays a positive role in maintaining the quality of service. Through the comparative test shown in Figure 8, we can find that the AMT-GAN has excellent visual effect. Therefore, in the service scenario where the image can only be slightly changed, we can add the AMT-GAN as an input of QM-VAE, so that QM-VAE has five inputs. Figure 12 shows the changing trend of model loss when the input number of QM-VAE is 3 (removing blindfold), 4 (used above), and 5 (adding AMT-GAN). It can be seen that the model loss reduction rate is improved after the addition of blindfold and AMT-GAN, which proves that our model can use existing technologies \begin{table}
\begin{tabular}{c c c} \hline \hline
**The Coefficient of MSE** & **Epochs = 20** & **Epochs = 40** \\ \hline
0 & 0.740 & 0.825 \\
0.2 & 0.23 & 0.18 \\
0.5 & 0.205 & 0.150 \\
0.8 & 0.225 & 0.165 \\
1 & 0.210 & 0.175 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Service quality loss rate of different expressions. Figure 11: The result of ignoring the MSE. Figure 12: Change of model loss under different inputs. according to the service scenarios to improve training efficiency, as well as having wide applicability and flexibility. Figure 13 demonstrates an example process of privacy protection and utility maintenance by QM-VAE. We tested the utility of the generated image and found that it produces the same result as the original image when it passes through the expression recognition machine, as shown in Figure 14. This proves that QM-VAE is effective in maintaining the work side of utility.