The widespread adoption of face recognition has led to increasing privacy
concerns, as unauthorized access to face images can expose sensitive personal
information. This paper explores face image protection against viewing and
recovery attacks. Inspired by image compression, we propose creating a visually
uninformative face image through feature subtraction between an original face
and its model-produced regeneration. Recognizable identity features within the
image are encouraged by co-training a recognition model on its high-dimensional
feature representation. To enhance privacy, the high-dimensional representation
is crafted through random channel shuffling, resulting in randomized
recognizable images devoid of attacker-leverageable texture details. We distill
our methodologies into a novel privacy-preserving face recognition method,
MinusFace. Experiments demonstrate its high recognition accuracy and effective
privacy protection. Its code is available at this https URL

基于图像压缩的灵感，本文提出了一种通过特征减法在原始面部图像和模型生成的再生图像之间创建视觉上无信息的面部图像的方法，通过对高维特征表示进行共同训练来鼓励图像中的可识别身份特征，通过随机通道混洗来增强隐私保护，得到了缺乏攻击者可利用的纹理细节的随机可辨认图像。我们将这些方法总结为一种新颖的隐私保护人脸识别方法 MinusFace，实验证明其具有高的识别准确性和有效的隐私保护。其代码可在此 URL 中获得。