Version 1
: Received: 24 March 2020 / Approved: 25 March 2020 / Online: 25 March 2020 (08:57:29 CET)
Version 2
: Received: 9 September 2020 / Approved: 13 September 2020 / Online: 13 September 2020 (11:16:17 CEST)
How to cite:
Kaihoko, Y.; Tan, P.X.; Kamioka, E. Prevention of Unintended Appearance in Photos Based on Human Behaviors Analysis. Preprints2020, 2020030376. https://doi.org/10.20944/preprints202003.0376.v2
Kaihoko, Y.; Tan, P.X.; Kamioka, E. Prevention of Unintended Appearance in Photos Based on Human Behaviors Analysis. Preprints 2020, 2020030376. https://doi.org/10.20944/preprints202003.0376.v2
Kaihoko, Y.; Tan, P.X.; Kamioka, E. Prevention of Unintended Appearance in Photos Based on Human Behaviors Analysis. Preprints2020, 2020030376. https://doi.org/10.20944/preprints202003.0376.v2
APA Style
Kaihoko, Y., Tan, P.X., & Kamioka, E. (2020). Prevention of Unintended Appearance in Photos Based on Human Behaviors Analysis. Preprints. https://doi.org/10.20944/preprints202003.0376.v2
Chicago/Turabian Style
Kaihoko, Y., Phan Xuan Tan and Eiji Kamioka. 2020 "Prevention of Unintended Appearance in Photos Based on Human Behaviors Analysis" Preprints. https://doi.org/10.20944/preprints202003.0376.v2
Abstract
Nowadays, with smartphones people can easily take photos, post photos to any social networks and use the photos for some purposes. This leads to a social problem that unintended appearance in photos may threaten the privacy of photographed person. Some solutions to protect facial privacy in photos have already been proposed. However, most of them rely on different techniques to de-identify photos which can be done only by photographers, giving no choice to photographed person. To deal with that, we propose an approach that allows photographed person to proactively detect whether someone is intentionally/unintentionally trying to take pictures of him/her. Thereby, he/she can have appropriate reaction to protect the privacy. In this approach, we assume that the photographed person uses a wearable camera to record the surrounding environment in real-time. The skeleton information of likely photographers who are captured in the monitoring video is then extracted to be put into the calculation of dynamic programming score which is eventually compared with a threshold for recognition of photo-taking behavior. Experimental results demonstrate that by using the proposed approach, the photo-taking behavior is precisely recognized with high accuracy of 92.5%.
Keywords
Photo-taking Behavior; photo capturing and sharing; bystanders; human behavior analysis; identity protection
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
