@online{steil2018_arxiv,
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},
LANGUAGE = {eng},
URL = {http://arxiv.org/abs/1801.04457},
EPRINT = {1801.04457},
EPRINTTYPE = {arXiv},
YEAR = {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.},
}