A deep dive into enhancing sharing of naturalistic driving data through face deidentification

Abstract

Human factors research in transportation relies on naturalistic driving studies (NDS) which collect real-world data from drivers on actual roads. NDS data offer valuable insights into driving behavior, styles, habits, and safety-critical events. However, these data often contain personally identifiable information (PII), such as driver face videos, which cannot be publicly shared due to privacy concerns. To address this, our paper introduces a comprehensive framework for deidentifying drivers' face videos, that can facilitate the wide sharing of driver face videos while protecting PII. Leveraging recent advancements in generative adversarial networks (GANs), we explore the efficacy of different face swapping algorithms in preserving essential human factors attributes while anonymizing participants' identities. Most face swapping algorithms are tested in restricted lighting conditions and indoor settings, there is no known study that tested them in adverse and natural situations. We conducted extensive experiments using large-scale outdoor NDS data, evaluating the quantification of errors associated with head, mouth, and eye movements, along with other attributes important for human factors research. Additionally, we performed qualitative assessments of these methods through human evaluators providing valuable insights into the quality and fidelity of the deidentified videos. We propose the utilization of synthetic faces as substitutes for real faces to enhance generalization. Additionally, we created practical guidelines for video deidentification, emphasizing error threshold creation, spot-checking for abrupt metric changes, and mitigation strategies for reidentification risks. Our findings underscore nuanced challenges in balancing data utility and privacy, offering valuable insights into enhancing face video deidentification techniques in NDS scenarios.