Learning Privacy-preserving Optics for Human Pose Estimation
Abstract
The widespread use of always-connected digital cameras in our everyday life has led to increasing concerns about the users’ privacy and security. How to develop privacy- preserving computer vision systems? In particular, we want to prevent the camera from obtaining detailed visual data that may contain private information. However, we also want the camera to capture useful information to perform computer vision tasks. Inspired by the trend of jointly designing optics and algorithms, we tackle the problem of privacy-preserving human pose estimation by optimizing an optical encoder (hardware-level protection) with a software decoder (convolutional neural network) in an end-to- end framework. We introduce a visual privacy protection layer in our optical encoder that, parametrized appropriately, enables the optimization of the camera lens’s point spread function (PSF). We validate our approach with extensive simulations and a prototype camera. We show that our privacy-preserving deep optics approach successfully degrades or inhibits private attributes while maintaining important features to perform human pose estimation.
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Hinojosa et al. [27] developed a privacy-preserving HPE method by designing an optical lens using deep optics that hides personal information while enabling HPE.
“However, Hinojosa et al. [27] developed a privacy-preserving HPE by designing an optical lens using deep optics that hides personal information while enabling HPE.”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [27]
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Abdulaziz, S., D’Amicantonio, G., & Bondarev, E. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00620 · Cited by 2 · see citing works
On the Design of Privacy-Aware Cameras: A Study on Deep Neural Networks
Carvalho, M., Ennaffi, O., Chateau, S., & Bachir, S. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_17 · Cited by 2 · see citing works
Privacy-Preserving Abnormal Gait Detection Using Computer Vision and Machine Learning
Naz, A., Prasad, P. S., Mccall, S., Chi Leung, C. H. A. N., Ochi, I., Gong, L., & Yu, M. (2025) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eetpht.11.9094 · Cited by 2 · see citing works
Ishige, M., Yoshimura, Y., & Yonetani, R. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247671 · Cited by 5 · see citing works
Enhancing privacy with optical element design for fall detection
Gong, L., Mccall, S., & Yu, M. (2023) · Electronics Letters · doi:10.1049/ell2.12995 · Cited by 2 · see citing works
PP-Pose: Privacy-Preserving Human Pose Estimation Using Random High-Frequency Channel Combinations
Zhao, H., Huang, H., & Li, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4969-6_15 · Cited by 1 · see citing works
Deep learning across optical system workflow: a comprehensive review from design to assembly
Nie, Y., Su, R., Thibault, S., Menke, C., Erps, J. V., Hugo Thienpont, A., & Ottevaere, H. (2026) · Journal of Physics Photonics · doi:10.1088/2515-7647/ae3f6b · Cited by 1 · see citing works
Privacy-Encrypted Lensless Camera for Face Recognition
Huang, Z., Shi, W., Wu, S., Liu, X., Qian, C., Liu, W., Yang, S., & Chen, H. (2023) · Asia Communications and Photonics Conference · doi:10.1109/acp/poem59049.2023.10368853 · Cited by 1 · see citing works
Dual Optical Design for VIS-NIR Classification based on Single Pixel and Diffractive Cameras
Jerez, A., Blanco, G., Urrea, S., García, H., Castillo, S., & Argüello, H. (2023) · IEEE Colombian Caribbean Conference (C3) · doi:10.1109/c358072.2023.10436309 · Cited by 1 · see citing works
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization
Aslam, N., & Nasrollahi, K. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00077 · Cited by 1 · see citing works
Data-Driven Optical Coding Optimization in Computational Imaging
Argüello, H., & Bacca, J. (2022) · Imaging and Applied Optics · doi:10.1364/cosi.2022.cw4b.1 · Cited by 1 · see citing works
Encoded Diffractive Optics for Hyperspectral Imaging
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Physics Based Camera Privacy: Lens and Network Co-Design to the Rescue
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Spectral Classification using a Dual Optical Setup and Deep Neural Networks
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Pre-capture Privacy via Adaptive Single-Pixel Imaging
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PrivateEye: In-Sensor Privacy Preservation Through Optical Feature Separation
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Adaptive Multi‐Lens Phase Modulation for Scale‐Aware Privacy‐Preserving Human Pose Recognition
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Privacy-Aware Meta-Optics for Person Detection
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Face De-Identification: A Domain-Centric Survey From Capture to Processing
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Cross-Attention Fusion of mmWave Radar and IMU for Robust 3D Human Pose Estimation
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SynHMR: Synergistic Joint-Mesh Modeling for LiDAR-based Human Mesh Reconstruction
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A Layer-Wise Mechanistic Analysis of Privacy in Vision Transformer Pose Estimators
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ViPoser: Sparse-IMU Based Human Pose Estimation with Distilled Vision Foundation Priors
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Survey on intelligent video surveillance
Elnady, M., Abdelmunim, H. E., Abidou, D., & Elmadah, I. (2026) · Journal Of Big Data · doi:10.1186/s40537-026-01560-z
- Authors
- Hinojosa, C., Niebles, J. C., & Arguello, H.
- Affiliations
- Universidad Industrial de Santander (CO)
- Stanford University (US)
- Universidad Industrial de Santander
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 2553-2562
- OpenAlex
- W3203180037
- Cited by
- 62
- References
- 57
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 12 · 2023: 12 · 2022: 12
- FWCI
- 4.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Keywords
- optical encoder, camera lens design, end-to-end optimization
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