# A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance
## 5 Experimentation and Results
### Images' Utility Maintenance  
We put one original image and four corresponding de-identified images together as a set of data for QM-VAE training. We used a set of training data as the input of the model and obtained the generated image corresponding to each set of data. In the process of training, Equation (7) is used as the loss function. In order to obtain the minimum loss rate, we conducted many experiments. We changed the parameter \(\alpha\) that determines the weights of the MSE and service quality loss in the loss function and then observed the model effect of service quality maintenance, respectively, to obtain the most appropriate composition of the loss function. The test set was used to calculate the expression loss rate for various \(\alpha\) values and training epochs. We obtained the changing trend of the expression loss rate as indicated in Figure 6.  
From the above results, we can consider that when \(\alpha\) is about 0.4, the effect of the model is the best. when training for 40 rounds, the loss rate of facial expression recognition is only 0.14, which is less than that of the four protected images, and the loss rate is reduced by at least 6.7%. This demonstrates that our approach has a remarkable effect on preserving the quality of face expressions. Compared with image fusion, which only considers the mean-squared error, the result of \(\alpha=0.4\) has more advantages in the number of training rounds, which reflects that the loss of the quality of service plays a significant role in the overall loss function. The resulting images are shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Blindfold** & **Mosaic** & **Laplace** & **Cartoon** \\ \hline Quality loss rate & 0.462 & 0.207 & 0.207 & 0.465 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Service quality loss rate under the four de-identification modes. Figure 5: The results of the de-identified image. Compared with the method without considering the specific loss of service quality, QM-VAE has advantages in maintaining service quality. We used AMT-GAN [6], one of the latest privacy protection technologies, to process the dataset images, and the image results are shown in Figure 8. The loss rate of the service image quality of AMT-GAN is 0.395, which is about 0.255 more than that of QM-VAE, which obviously shows that QM-VAE performs better in maintaining the quality of service. We used the model trained for 40 epochs when \(\alpha\) is 0.4 for the following analysis. We measured the loss rate of six kinds of facial expressions under different methods. The results are shown in Table 3.  
The service quality of each expression was maintained to some extent under the QM-VAE treatment, and the loss rate of the fear group and surprise group was slightly higher because of the small sample size. We calculated the difference of the values corresponding to the facial expression recognition results before and after being processed by the four selected methods, AMT-GAN and QM-VAE, and drew error scatter plots and error bar diagrams of data, as shown in Figures 9 and 10. Compared with the other five methods, QM-VAE shows a more stable and excellent utility maintenance ability when the results of facial expression recognition are diverse. In the problem of face recognition, we also hope to make the smallest possible changes to the image in the process, that is to erase only the private information and maintain its similarity to the original image as much as possible, which is beneficial to maintaining the effectiveness of the image. We used the MSE to calculate the degree of changes to the image. If you set the coefficient of the MSE in the loss function to 0 (set the value of \(\alpha\) to 1), the resulting image is as shown in Figure 11. From the resulting images represented by these three images, we cannot recognize faces, let alone extract their expression information. It can be considered that the maintenance of image utility is ineffective in this case.