Privacy-Preserving Eye Videos using Rubber Sheet Model

Abstract

Video-based eye trackers estimate gaze based on eye images/videos. As security and privacy concerns loom over technological advancements, tackling such challenges is crucial. We present a new approach to handle privacy issues in eye videos by replacing the current identifiable iris texture with a different iris template in the video capture pipeline based on the Rubber Sheet Model. We extend to image blending and median-value representations to demonstrate that videos can be manipulated without significantly degrading segmentation and pupil detection accuracy.

Will be published in ETRA 20 Short Papers, June 2-5, 2020, Stuttgart, Germany Copyright 2020 Association for Computing Machinery

References in corpus (8)

• Appearance-Based Gaze Estimation in the Wild
• Deep Pictorial Gaze Estimation
• Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings
• RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking
• Privacy-Aware Eye Tracking Using Differential Privacy
• PrivacEye: Privacy-Preserving Head-Mounted Eye Tracking Using Egocentric Scene Image and Eye Movement Features
• Differential Privacy for Eye-Tracking Data
• Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework

Cited by in corpus (4)

• A privacy-preserving approach to streaming eye-tracking data
• Differential Privacy for Eye Tracking with Temporal Correlations
• Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer
• Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study