Faceless Person Recognition: Privacy Implications in Social Media View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2016-09-17

AUTHORS

Seong Joon Oh , Rodrigo Benenson , Mario Fritz , Bernt Schiele

ABSTRACT

As we shift more of our lives into the virtual domain, the volume of data shared on the web keeps increasing and presents a threat to our privacy. This works contributes to the understanding of privacy implications of such data sharing by analysing how well people are recognisable in social media data. To facilitate a systematic study we define a number of scenarios considering factors such as how many heads of a person are tagged and if those heads are obfuscated or not. We propose a robust person recognition system that can handle large variations in pose and clothing, and can be trained with few training samples. Our results indicate that a handful of images is enough to threaten users’ privacy, even in the presence of obfuscation. We show detailed experimental results, and discuss their implications. More... »

PAGES

19-35

Book

TITLE

Computer Vision – ECCV 2016

ISBN

978-3-319-46486-2
978-3-319-46487-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-46487-9_2

DOI

http://dx.doi.org/10.1007/978-3-319-46487-9_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049316471


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