  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 https://github.com/Tencent/TFace.
