# A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance
## 1 Introduction  
In recent years, with the advancement of imaging devices and fast-transmission media, high-quality video surveillance has been popularized. People's identity and behavior information are always exposed to video surveillance in public spaces. While the intensive surveillance distribution has contributed towards making people's lives more convenient in many ways, care needs to be taken as a huge amount of image sources is being stored, transmitted, or published around the world, and they also become easily accessible and more likely to be abused. The face is significant to reveal a person's most distinguishing features in images [1], so the abuse of facial images in surveillance can violate the safety of a person's privacy. In practice, face de-identification cannot be simply described as the complete elimination of private information, but it must also take into account the real utility of the image. If the image is protected by simple methods such as blurring or occlusion, along with identity, other vital non-biometric traits may be buried. Thus, the de-identified image will lose its original value in the video surveillance of different service situations, such as services that are sensitive to facial emotion or facial shape, etc. As a result, protecting the privacy data in a face image and maintaining the application quality of the de-recognized image in the service system at the same time become crucial. For example, privacy protection iscrucial in social settings, but if useful information such as facial expressions can be retained in the meantime, it will considerably contribute to the total expression of information. Nevertheless, the link between the facial privacy features to be removed and the useful information to be retained is often nonlinear and sometimes even contradictory, which is the conundrum that the present facial de-identification research is confronted with. As a result, figuring out how to achieve the aims of protecting privacy while retaining quality is a challenge. Face image de-identification includes obscuring the identifying information in the face image, on the one hand, while having the resulting images have a similar effect to the original image in the service, on the other hand. Many recent research works have focused on achieving the aforementioned ideal equilibrium. Reference [2] suggested a technique of face de-identification that depends on the detection of the face and key points, followed by variational adaptive filtering, to achieve the face de-identification while preserving expression. Reference [3] devised unique verifying and regulating modules in the process of face de-identification to ensure that de-identified photos maintain structure similarity. Reference [4] fine-tuned the encoding section of the typical autoencoder and completed the de-identification operation in the latent space, allowing the original face pictures to retain their nature and variety while creating new faces with various identity labels. These technologies are oriented toward one specific quality standard (such as facial expressions) [5], and there is not yet a model that can automatically adjust the process of generating images according to the needs of different service evaluations. The need to develop an effective face de-identification model for privacy protection cannot be overstated. To alleviate the above-mentioned constraints, we build on previous techniques and propose a novel de-identification strategy called Quality Maintenance-Variational AutoEncoder (QM-VAE) in this paper. The proposed strategy makes use of a powerful generative model, Vector Quantized Variational Autoencoder (VQ-VAE) [6], and is capable of producing high-quality de-identified faces. We process the images, respectively, using four typical protection methods, resulting in four sets of images without private information. Then, for specific services, we modify the loss function to establish a service quality evaluation index that guides the merging of these images.