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.

本文旨在通过研究社交媒体数据中人员识别的准确性，为我们日益涉足虚拟空间，隐私受到威胁的新现实提供理解。我们定义了多种场景并提出了一种强大的人员识别系统，证明即便在人脸被模糊处理的情况下，仍然能威胁到用户的隐私安全。本文详细论述了我们的实验结果及其涵义。