FaceOff: Anonymizing Videos in the Operating Rooms

TL;DR

This paper presents a deep learning-based method for anonymizing surgical videos by detecting and blurring faces, including those partially obscured by masks and caps, to protect patient and staff privacy.

Contribution

It introduces a tailored dataset and a temporal regularization technique to improve face detection accuracy in the challenging surgical environment.

Findings

Achieved 88.05% face detection recall before temporal smoothing.

Improved recall to 93.45% with temporal regularization.

Demonstrated effective face anonymization in OR videos.

Abstract

Video capture in the surgical operating room (OR) is increasingly possible and has potential for use with computer assisted interventions (CAI), surgical data science and within smart OR integration. Captured video innately carries sensitive information that should not be completely visible in order to preserve the patient's and the clinical teams' identities. When surgical video streams are stored on a server, the videos must be anonymized prior to storage if taken outside of the hospital. In this article, we describe how a deep learning model, Faster R-CNN, can be used for this purpose and help to anonymize video data captured in the OR. The model detects and blurs faces in an effort to preserve anonymity. After testing an existing face detection trained model, a new dataset tailored to the surgical environment, with faces obstructed by surgical masks and caps, was collected for fine-tuning to achieve higher face-detection rates in the OR. We also propose a temporal regularisation kernel to improve recall rates. The fine-tuned model achieves a face detection recall of 88.05 % and 93.45 % before and after applying temporal-smoothing respectively.