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.
@article{arxiv.1607.08438,
title = {Faceless Person Recognition; Privacy Implications in Social Media},
author = {Seong Joon Oh and Rodrigo Benenson and Mario Fritz and Bernt Schiele},
journal= {arXiv preprint arXiv:1607.08438},
year = {2016}
}