  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.
