LAUSR

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