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PrivacEye: Privacy-Preserving First-Person Vision Using Image Features and Eye Movement Analysis

Julian Steil, Marion Koelle, Wilko Heuten, Susanne Boll, Andreas Bulling

arXiv:1801.04457, 2018.


Abstract

As first-person cameras in head-mounted displays become increasingly prevalent, so does the problem of infringing user and bystander privacy. To address this challenge, we present PrivacEye, a proof-of-concept system that detects privacysensitive everyday situations and automatically enables and disables the first-person camera using a mechanical shutter. To close the shutter, PrivacEye detects sensitive situations from first-person camera videos using an end-to-end deep-learning model. To open the shutter without visual input, PrivacEye uses a separate, smaller eye camera to detect changes in users’ eye movements to gauge changes in the "privacy level" of the current situation. We evaluate PrivacEye on a dataset of first-person videos recorded in the daily life of 17 participants that they annotated with privacy sensitivity levels. We discuss the strengths and weaknesses of our proof-of-concept system based on a quantitative technical evaluation as well as qualitative insights from semi-structured interviews.

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BibTeX

@techreport{steil18_arxiv_2, title = {PrivacEye: Privacy-Preserving First-Person Vision Using Image Features and Eye Movement Analysis}, author = {Steil, Julian and Koelle, Marion and Heuten, Wilko and Boll, Susanne and Bulling, Andreas}, year = {2018} }