GAN-based Deidentification of Drivers' Face Videos: An Assessment of
  Human Factors Implications in NDS Data
Surendrabikram Thapa Abhijit Sarkar
This paper addresses the problem of sharing drivers' face videos for
transportation research while adhering to proper ethical guidelines. The paper
first gives an overview of the multitude of problems associated with sharing
such data and then proposes a framework on how artificial intelligence-based
techniques, specifically face swapping, can be used for de-identifying drivers'
faces. Through extensive experimentation with an Oak Ridge National Laboratory
(ORNL) dataset, we demonstrate the effectiveness of face-swapping algorithms in
preserving essential attributes related to human factors research, including
eye movements, head movements, and mouth movements. The efficacy of the
framework was also tested on various naturalistic driving study data collected
at the Virginia Tech Transportation Institute. The results achieved through the
proposed techniques were evaluated qualitatively and quantitatively using
various metrics. Finally, we discuss possible measures for sharing the
de-identified videos with the greater research community.