Faceless Person Recognition; Privacy Implications in Social Media

Autor:	Oh, Seong Joon, Benenson, Rodrigo, Fritz, Mario, Schiele, Bernt
Rok vydání:	2016
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Cryptography and Security
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
Externí odkaz:	http://arxiv.org/abs/1607.08438 Zobrazit plný text záznamu View this record from Arxiv