@article{WOS:000419020100007,
abstract = {The authors introduce a system for person de-identification in video
data that de-identifies biometric and non-biometric features, namely
faces, hairstyles and clothing colours. The authors' system detects
human faces and silhouettes in the input video and replaces the detected
faces with random synthesised faces obtained using a deep convolutional
generative adversarial network. Alternative hairstyles are rendered over
the synthesised faces, and the human silhouette is recoloured so that
skin hues are preserved and clothing hues are altered. Through the use
of artificially synthesised faces that look realistic, they ensure that
the de-identified image looks natural and at the same time avoid ethical
and legal considerations present when using real face images as
replacement faces. As they address non-biometric feature
de-identification, their system offers a considerably higher level of
privacy protection than commonly employed solutions that use simple
image processing techniques such as blurring. Qualitative and
quantitative evaluation suggests that their system produces
de-identified images that look natural, at the same time being resistant
to re-identification attacks.},
address = {MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND},
author = {Brkic, Karla and Hrkac, Tomislav and Kalafatic, Zoran and Sikiric, Ivan},
doi = {10.1049/iet-spr.2017.0048},
issn = {1751-9675},
journal = {IET SIGNAL PROCESSING},
keywords = {face recognition; video signal processing; image s},
month = dec,
number = {9},
pages = {1062--1068},
publisher = {INST ENGINEERING TECHNOLOGY-IET},
title = {{Face, hairstyle and clothing colour de-identification in video sequences}},
type = {Article},
volume = {11},
year = {2017}
}

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