Generative AI for Medical Video De-Identification

Sharing medical data for research purposes is presenting significant challenges due to patient privacy concerns. Specifically, within the field of Mental and Behavioral Health (MBH), these privacy concerns are particularly pronounced. In this work we focus on video MBH data, containing interviewed patients. Conventional techniques for de-identifying faces in such videos tend to completely eliminate facial information, rendering the development of algorithms reliant on facial analysis unfeasible. To address this issue, we propose the utilization of Generative AI to anonymize MBH videos, eliminating identifying information while retaining essential behavioral characteristics necessary for diagnoses. Our approach involves a two-stage process. We start by synthesizing an alternative first frame containing a face with a new identity, while preserving attributes from the original video such as pose, facial structure and mood. Subsequently, we animate this frame by generating macro and micro-expressions aligned with the fine motion patterns observed in the source video. The first stage employs a conditional latent Diffusion model, while the second stage leverages the First Order Motion Model algorithm. Two applications for our approach are presented: MBH dataset de-identification and synthetic dataset generation.