Prevention of Unintended Appearance in Photos Based on Human Behavior Analysis

Prevention of Unintended Appearance in Photos Based on Human Behavior Analysis

Nowadays, with smartphones, people can easily take photos, post photos to any social networks, and use the photos for various purposes. This leads to a social problem that unintended appearance in photos may threaten the facial privacy of photographed people. Some solutions to protect facial privacy...

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Bibliographic Details
Published in:Information (Basel) Vol. 11; no. 10; p. 468
Main Authors: Kaihoko, Yuhi, Tan, Phan Xuan, Kamioka, Eiji
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-10-2020
Subjects:
bystanders
Cameras
Cellular telephones
Cooperation
Dynamic programming
facial privacy
Human behavior
human behavior analysis
identity protection
Machine learning
Methods
photo capturing and sharing
photo-taking behavior
Privacy
Smartphones
Social networks
Social problems
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Summary:Nowadays, with smartphones, people can easily take photos, post photos to any social networks, and use the photos for various purposes. This leads to a social problem that unintended appearance in photos may threaten the facial privacy of photographed people. 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 a photographed person to proactively detect whether someone is intentionally/unintentionally trying to take pictures of him. Thereby, he can have appropriate reaction to protect the facial 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 and 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%.
ISSN:2078-2489
2078-2489
DOI:10.3390/info11100468