  We propose a reversible face de-identification method for low resolution
video data, where landmark-based techniques cannot be reliably used. Our
solution is able to generate a photo realistic de-identified stream that meets
the data protection regulations and can be publicly released under minimal
privacy constraints. Notably, such stream encapsulates all the information
required to later reconstruct the original scene, which is useful for
scenarios, such as crime investigation, where the identification of the
subjects is of most importance. We describe a learning process that jointly
optimizes two main components: 1) a public module, that receives the raw data
and generates the de-identified stream, where the ID information is surrogated
in a photo-realistic and seamless way; and 2) a private module, designed for
legal/security authorities, that analyses the public stream and reconstructs
the original scene, disclosing the actual IDs of all the subjects in the scene.
The proposed solution is landmarks-free and uses a conditional generative
adversarial network to generate synthetic faces that preserve pose, lighting,
background information and even facial expressions. Also, we enable full
control over the set of soft facial attributes that should be preserved between
the raw and de-identified data, which broads the range of applications for this
solution. Our experiments were conducted in three different visual surveillance
datasets (BIODI, MARS and P-DESTRE) and showed highly encouraging results. The
source code is available at https://github.com/hugomcp/uu-net.
