Multi-view Consensus CNN for 3D Facial Landmark Placement

Publikation: Bidrag til bog/antologi/rapportBidrag til bog/antologiForskningfagfællebedømt

The rapid increase in the availability of accurate 3D scanning devices has moved facial recognition and analysis into the 3D domain. 3D facial landmarks are often used as a simple measure of anatomy and it is crucial to have accurate algorithms for automatic landmark placement. The current state-of-the-art approaches have yet to gain from the dramatic increase in performance reported in human pose tracking and 2D facial landmark placement due to the use of deep convolutional neural networks (CNN). Development of deep learning approaches for 3D meshes has given rise to the new subfield called geometric deep learning, where one topic is the adaptation of meshes for the use of deep CNNs. In this work, we demonstrate how methods derived from geometric deep learning, namely multi-view CNNs, can be combined with recent advances in human pose tracking. The method finds 2D landmark estimates and propagates this information to 3D space, where a consensus method determines the accurate 3D face landmark position. We utilise the method on a standard 3D face dataset and show that it outperforms current methods by a large margin. Further, we demonstrate how models trained on 3D range scans can be used to accurately place anatomical landmarks in magnetic resonance images.
OriginalsprogEngelsk
Titel Computer Vision – ACCV 2018 : 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part I
ForlagSpringer
Publikationsdato2019
Sider706-719
ISBN (Trykt)9783030208868
ISBN (Elektronisk)9783030208875
DOI
StatusUdgivet - 2019
Begivenhed14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australien
Varighed: 2 dec. 20186 dec. 2018

Konference

Konference14th Asian Conference on Computer Vision, ACCV 2018
LandAustralien
ByPerth
Periode02/12/201806/12/2018
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind11361 LNCS

ID: 225715641