Due to the availability of low-cost internet and other data transmission media, a high volume of multimedia data get shared very quickly. Often, the identity of individuals gets revealed through images or videos without their consent, which affects their privacy. Since face is the only biometric feature that reveals the most identifiable characteristics of a person in an image or a video frame, the need for the development of an effective face de-identification algorithm for privacy preservation cannot be over-emphasized. Existing solutions to face de-identification are either non-formal or are unable to obfuscate identifiable features completely. In this paper, we propose an automated face de-identification algorithm that takes as input a facial image and generates a new face that preserves the emotion and non-biometric facial attributes of a target face. We consider a proxy set of a large collection of artificial faces generated by StyleGAN and select the most appropriate face from the proxy set that has a facial expression and pose similar to that of the target face. The faces in the proxy set are artificially generated, and hence the face selected from this set is completely anonymous. To retain the non-biometric attributes of the target face, we employ a generative adversarial network (GAN) with a suitable loss function that fuses the non-biometric attributes of the target face with the face selected from the proxy set to obtain the final de-identified face. Experimental results emphasize the superiority of our approach over state-of-the-art face de-identification methods.

中文翻译：

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通过面部去识别化保护隐私并同时保护情绪

由于低成本互联网和其他数据传输媒体的可用性，大量多媒体数据可以非常快速地共享。通常，个人身份会在未经他们同意的情况下通过图像或视频泄露，这会影响他们的隐私。由于人脸是唯一能在图像或视频帧中显示出一个人最可识别特征的生物特征，因此开发有效的人脸去识别算法以保护隐私的必要性再怎么强调也不为过。现有的面部去识别化解决方案要么是非正式的，要么无法完全混淆可识别的特征。在本文中，我们提出了一种自动面部去识别算法，该算法将面部图像作为输入并生成保留目标面部情感和非生物特征面部属性的新面部。我们考虑由 StyleGAN 生成的大量人造人脸的代理集，并从代理集中选择最合适的人脸，其面部表情和姿势与目标人脸相似。代理集中的人脸是人工生成的，因此从该集中选择的人脸是完全匿名的。为了保留目标人脸的非生物特征属性，我们采用了一个具有合适损失函数的生成对抗网络（GAN），该网络将目标人脸的非生物特征与从代理集中选择的人脸融合以获得最终的de - 识别的脸。