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 which, parametrized appropriately, enables the optimization of the point spread function (PSF) of the camera lens. 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. See our Project Page!

Publication
In 2021 International Conference on Computer Vision
Privacy Preserving Computational Photography Computational Imaging Human Pose Estimation Computer Vision
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Carlos Hinojosa
Ph.D Student In Computer Science

I’m Carlos Hinojosa. Computer scientist and engineer with over six years of experience in scientific research and software development. My research interests are in computer vision, machine learning, computational imaging, sparse representation, and signal processing.