Faceless Person Recognition; Privacy Implications in Social Media
It addresses privacy concerns for social media users, but the approach is incremental as it builds on existing recognition methods.
This paper tackles the problem of privacy threats from social media data by analyzing how well people are recognizable in such data, finding that even a handful of images can threaten user privacy, even with obfuscation.
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.