Journal
IET SIGNAL PROCESSING
Volume 11, Issue 9, Pages 1046-1054Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-spr.2017.0049
Keywords
face recognition; neural nets; generative deep neural networks; image blurring; formal anonymity models; artificial surrogate faces; novel face deidentification pipeline; GNN; automated recognition tools
Categories
Funding
- ARRS (Slovenian Research Agency) Research Programme [P2-0250]
- ARRS Research Programme [P2-0214]
- TUBITAK project [113E067]
- Marie Curie FP7 Integration Grant within the 7th EU Framework Programme
- Croatian HRRZ project DePPSS De-identification for Privacy Protection in Surveillance Systems [6733]
- COST Action on De-identification for privacy protection in multimedia content [IC 1206]
Ask authors/readers for more resources
Face deidentification is an active topic amongst privacy and security researchers. Early deidentification methods relying on image blurring or pixelisation have been replaced in recent years with techniques based on formal anonymity models that provide privacy guaranties and retain certain characteristics of the data even after deidentification. The latter aspect is important, as it allows the deidentified data to be used in applications for which identity information is irrelevant. In this work, the authors present a novel face deidentification pipeline, which ensures anonymity by synthesising artificial surrogate faces using generative neural networks (GNNs). The generated faces are used to deidentify subjects in images or videos, while preserving non-identity-related aspects of the data and consequently enabling data utilisation. Since generative networks are highly adaptive and can utilise diverse parameters (pertaining to the appearance of the generated output in terms of facial expressions, gender, race etc.), they represent a natural choice for the problem of face deidentification. To demonstrate the feasibility of the authors' approach, they perform experiments using automated recognition tools and human annotators. Their results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is effective.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available