  While convenient in daily life, face recognition technologies also raise
privacy concerns for regular users on the social media since they could be used
to analyze face images and videos, efficiently and surreptitiously without any
security restrictions. In this paper, we investigate the face privacy
protection from a technology standpoint based on a new type of customized
cloak, which can be applied to all the images of a regular user, to prevent
malicious face recognition systems from uncovering their identity.
Specifically, we propose a new method, named one person one mask (OPOM), to
generate person-specific (class-wise) universal masks by optimizing each
training sample in the direction away from the feature subspace of the source
identity. To make full use of the limited training images, we investigate
several modeling methods, including affine hulls, class centers, and convex
hulls, to obtain a better description of the feature subspace of source
identities. The effectiveness of the proposed method is evaluated on both
common and celebrity datasets against black-box face recognition models with
different loss functions and network architectures. In addition, we discuss the
advantages and potential problems of the proposed method. In particular, we
conduct an application study on the privacy protection of a video dataset,
Sherlock, to demonstrate the potential practical usage of the proposed method.
Datasets and code are available at https://github.com/zhongyy/OPOM.
