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FaceOff: Anonymizing videos in the operating rooms

Flouty, E; Zisimopoulos, O; Stoyanov, D; (2018) FaceOff: Anonymizing videos in the operating rooms. In: Stoyanov, D and Taylor, Z and Sarikaya, D and McLeod, J and González Ballester, MA and Codella, NCF and Martell, A and Maier-Hein, L and Malpani, A and Zenati, MA and De Ribaupierre, S and Xiongbiao, L and Collins, T and Reichl, T and Drechsler, K and Erdt, M and Linguraru, MG and Oyarzun Laura, C and Shekhar, R and Wesarg, S and Emre Celebi, M and Dana, K and Halpern, A, (eds.) OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis (CARE 2018, CLIP 2018, OR 2.0 2018, ISIC 2018): Proceedings. (pp. pp. 30-38). Springer: Cham, Switzerland. Green open access

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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.

Type: Proceedings paper
Title: FaceOff: Anonymizing videos in the operating rooms
Event: First International Workshop, OR 2.0 2018; 5th International Workshop, CARE 2018; 7th International Workshop, CLIP 2018; Third International Workshop, ISIC 2018, held in Conjunction with MICCAI 2018, 16-20 September 2018, Granada, Spain
ISBN-13: 9783030012007
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-01201-4_4
Publisher version: https://doi.org/10.1007/978-3-030-01201-4_4
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Anonymization, Face detection, Surgical data science, Smart ORs
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10060024
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