Deep cascaded Bi-network for face hallucination
Refereed conference paper presented and published in conference proceedings


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AbstractWe present a novel framework for hallucinating faces of unconstrained poses and with very low resolution (face size as small as 5pxIOD). In contrast to existing studies that mostly ignore or assume pre-aligned face spatial configuration (e.g. facial landmarks localization or dense correspondence field), we alternatingly optimize two complementary tasks, namely face hallucination and dense correspondence field estimation, in a unified framework. In addition, we propose a new gated deep bi-network that contains two functionality-specialized branches to recover different levels of texture details. Extensive experiments demonstrate that such formulation allows exceptional hallucination quality on in-the-wild low-res faces with significant pose and illumination variations.
All Author(s) ListZhu S., Liu S., Loy C.C., Tang X.
Name of ConferenceEuropean Conference on Computer
Start Date of Conference08/10/2016
End Date of Conference16/10/2016
Place of ConferenceAmsterdam
Country/Region of ConferenceNetherlands
Proceedings TitleECCV 2016: Computer Vision – ECCV 2016
Detailed descriptionorganized by University of Amsterdam,
Year2016
Month1
Day1
Volume Number9909 LNCS
PublisherSpringer Verlag
Place of PublicationGermany
Pages614 - 630
ISBN9783319464534
ISSN1611-3349
LanguagesEnglish-United Kingdom

Last updated on 2020-30-06 at 04:22