In this paper, we propose a new face de-identification method based on generative adversarial network (GAN) to protect visual facial privacy, which is an end-to-end method (herein, FPGAN). First, we propose FPGAN and mathematically prove its convergence. Then, a generator with an improved U-Net is used to enhance the quality of the generated image, and two discriminators with a seven-layer network architecture are designed to strengthen the feature extraction ability of FPGAN. Subsequently, we propose the pixel loss, content loss, adversarial loss functions and optimization strategy to guarantee the performance of FPGAN. In our experiments, we applied FPGAN to face de-identification in social robots and analyzed the related conditions that could affect the model. Moreover, we proposed a new face de-identification evaluation protocol to check the performance of the model. This protocol can be used for the evaluation of face de-identification and privacy protection. Finally, we tested our model and four other methods on the CelebA, MORPH, RaFD, and FBDe datasets. The results of the experiments show that FPGAN outperforms the baseline methods.

在本文中，我们提出了一种新的基于生成对抗网络（GAN）的人脸识别方法，以保护视觉人脸隐私，这是一种端到端方法（在本文中称为FPGAN）。首先，我们提出FPGAN并通过数学证明其收敛性。然后，使用具有改进的U-Net的生成器来增强生成的图像的质量，并设计两个具有七层网络体系结构的鉴别器以增强FPGAN的特征提取能力。随后，我们提出了像素损失，内容损失，对抗损失函数和优化策略，以保证FPGAN的性能。在我们的实验中，我们将FPGAN应用于社交机器人中的去识别，并分析了可能影响模型的相关条件。此外，我们提出了一种新的人脸去识别评估协议来检查模型的性能。该协议可用于评估人脸识别和隐私保护。最后，我们在CelebA，MORPH，RaFD和FBDe数据集上测试了模型和其他四种方法。实验结果表明，FPGAN优于基线方法。