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


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.

Data Set

Data set and supplementary material are available from


Press Coverage | Mentions

  • 01.09.2018 | Privacy News Online: "How putting artificial intelligence in Google Glass-like systems could both help and harm our privacy"  
  • 25.01.2018 | New Scientist: "How do you stop your smart glasses filming in the toilet?"