Faceless Person Recognition; Privacy Implications in Social Media
Autor: | Oh, Seong Joon, Benenson, Rodrigo, Fritz, Mario, Schiele, Bernt |
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Rok vydání: | 2016 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | 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. Comment: Accepted to ECCV'16 |
Databáze: | arXiv |
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