  Data sharing for medical research has been difficult as open-sourcing
clinical data may violate patient privacy. Traditional methods for face
de-identification wipe out facial information entirely, making it impossible to
analyze facial behavior. Recent advancements on whole-body keypoints detection
also rely on facial input to estimate body keypoints. Both facial and body
keypoints are critical in some medical diagnoses, and keypoints invariability
after de-identification is of great importance. Here, we propose a solution
using deepfake technology, the face swapping technique. While this swapping
method has been criticized for invading privacy and portraiture right, it could
conversely protect privacy in medical video: patients' faces could be swapped
to a proper target face and become unrecognizable. However, it remained an open
question that to what extent the swapping de-identification method could affect
the automatic detection of body keypoints. In this study, we apply deepfake
technology to Parkinson's disease examination videos to de-identify subjects,
and quantitatively show that: face-swapping as a de-identification approach is
reliable, and it keeps the keypoints almost invariant, significantly better
than traditional methods. This study proposes a pipeline for video
de-identification and keypoint preservation, clearing up some ethical
restrictions for medical data sharing. This work could make open-source high
quality medical video datasets more feasible and promote future medical
research that benefits our society.
