# DisguisOR: Holistic Face Anonymization for the Operating Room

arXiv: https://arxiv.org/abs/2307.14241 · HTML: https://mlsanity.com/paper/2307.14241/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 38 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 24 of 24)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [Convolutional Mesh Regression for Single-Image Human Shape Reconstruction](https://mlsanity.com/paper/1905.03244/) (2019)
- [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://mlsanity.com/paper/1706.08500/) (2017)
- [TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks](https://mlsanity.com/paper/2003.10751/) (2020)
- [DSFD: Dual Shot Face Detector](https://mlsanity.com/paper/1810.10220/) (2018)
- [Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression](https://mlsanity.com/paper/2104.02300/) (2021)
- [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation](https://mlsanity.com/paper/1808.08180/) (2018)
- [4D-OR: Semantic Scene Graphs for OR Domain Modeling](https://mlsanity.com/paper/2203.11937/) (2022)
- [A Hybrid Model for Identity Obfuscation by Face Replacement](https://mlsanity.com/paper/1804.04779/) (2018)
- [FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization](https://mlsanity.com/paper/1811.10136/) (2018)
- [Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery](https://mlsanity.com/paper/2006.16166/) (2020)
- [TinaFace: Strong but Simple Baseline for Face Detection](https://mlsanity.com/paper/2011.13183/) (2020)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [Going Deeper Into Face Detection: A Survey](https://mlsanity.com/paper/2103.14983/) (2021)
- [Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach](https://mlsanity.com/paper/1811.12296/) (2018)
- [Know your sensORs -- A Modality Study For Surgical Action Classification](https://mlsanity.com/paper/2203.08674/) (2022)
- [FaceOff: Anonymizing Videos in the Operating Rooms](https://mlsanity.com/paper/1808.04440/) (2018)
- [VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment](https://mlsanity.com/paper/2004.06239/) (2020)
- [3D Face Recognition: A Survey](https://mlsanity.com/paper/2108.11082/) (2021)

## Built on by (most cited first, 9 of 9)

- [MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical Environments](https://mlsanity.com/paper/2503.02579/) (2025)
- [Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room](https://mlsanity.com/paper/2503.13028/) (2025)
- [Non-Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunities](https://mlsanity.com/paper/2604.01274/) (2026)
- [Privacy-Preserving Operating Room Workflow Analysis using Digital Twins](https://mlsanity.com/paper/2504.12552/) (2025)
- [TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking](https://mlsanity.com/paper/2508.07968/) (2025)
- [Reality's Canvas, Language's Brush: Crafting 3D Avatars from Monocular Video](https://mlsanity.com/paper/2312.04784/) (2023)
- [Mitigating Biases in Surgical Operating Rooms with Geometry](https://mlsanity.com/paper/2508.08028/) (2025)
- [Self-Supervised Uncalibrated Multi-View Video Anonymization in the Operating Room](https://mlsanity.com/paper/2602.02850/) (2026)
- [DisPOSE: Projected Polystochastic Diffusion for Self-Supervised Multi-View 3D Human Pose Estimation](https://mlsanity.com/paper/2606.07419/) (2026)

## Abstract (arXiv)

Purpose: Recent advances in Surgical Data Science (SDS) have contributed to an increase in video recordings from hospital environments. While methods such as surgical workflow recognition show potential in increasing the quality of patient care, the quantity of video data has surpassed the scale at which images can be manually anonymized. Existing automated 2D anonymization methods under-perform in Operating Rooms (OR), due to occlusions and obstructions. We propose to anonymize multi-view OR recordings using 3D data from multiple camera streams. Methods: RGB and depth images from multiple cameras are fused into a 3D point cloud representation of the scene. We then detect each individual's face in 3D by regressing a parametric human mesh model onto detected 3D human keypoints and aligning the face mesh with the fused 3D point cloud. The mesh model is rendered into every acquired camera view, replacing each individual's face. Results: Our method shows promise in locating faces at a higher rate than existing approaches. DisguisOR produces geometrically consistent anonymizations for each camera view, enabling more realistic anonymization that is less detrimental to downstream tasks. Conclusion: Frequent obstructions and crowding in operating rooms leaves significant room for improvement for off-the-shelf anonymization methods. DisguisOR addresses privacy on a scene level and has the potential to facilitate further research in SDS.