PrivacEye:保护隐私的头戴式眼动追踪,使用以自我为中心的场景图像和眼动特征

Julian Steil, Marion Koelle, Wilko Heuten, Susanne CJ Boll, A. Bulling
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引用次数: 59

摘要

增强现实显示器等眼镜设备越来越多地集成了眼动追踪功能,但将用户的视线映射到视觉场景所需的第一人称摄像头可能会对用户和旁观者的隐私构成重大威胁。我们介绍了PrivacEye,一种检测隐私敏感的日常情况的方法,并使用机械快门自动启用和禁用眼动仪的第一人称摄像头。为了在隐私敏感的情况下关闭快门,该方法使用了第一人称视频的深度表示,并结合了对用户眼球运动进行编码的丰富特征。为了在没有视觉输入的情况下打开快门,PrivacEye仅检测用户眼球运动的变化,以衡量当前情况下“隐私水平”的变化。我们在17名参与者的第一人称视频数据集上评估了我们的方法,这些数据集记录了他们的日常生活情况,并对隐私敏感性进行了注释,结果表明我们的方法在这种具有挑战性的环境中有效地保护了隐私。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PrivacEye: privacy-preserving head-mounted eye tracking using egocentric scene image and eye movement features
Eyewear devices, such as augmented reality displays, increasingly integrate eye tracking, but the first-person camera required to map a user's gaze to the visual scene can pose a significant threat to user and bystander privacy. We present PrivacEye, a method to detect privacy-sensitive everyday situations and automatically enable and disable the eye tracker's first-person camera using a mechanical shutter. To close the shutter in privacy-sensitive situations, the method uses a deep representation of the first-person video combined with rich features that encode users' eye movements. To open the shutter without visual input, PrivacEye detects changes in users' eye movements alone to gauge changes in the "privacy level" of the current situation. We evaluate our method on a first-person video dataset recorded in daily life situations of 17 participants, annotated by themselves for privacy sensitivity, and show that our method is effective in preserving privacy in this challenging setting.
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