Random Forests for Real Time 3D Face Analysis

We present a random forest-based framework for real time head pose estimation from depth images and extend it to localize a set of facial features in 3D. Our algorithm takes a voting approach, where each patch extracted from the depth image can directly cast a vote for the head pose or each of the f...

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Veröffentlicht in:International journal of computer vision Jg. 101; H. 3; S. 437 - 458
Hauptverfasser: Fanelli, Gabriele, Dantone, Matthias, Gall, Juergen, Fossati, Andrea, Van Gool, Luc
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.02.2013
Springer
Springer Nature B.V
Schlagworte:
3-D graphics
Algorithms
Analysis
Artificial Intelligence
Computer graphics
Computer Imaging
Computer Science
Computer vision
Datasets
Face
Facial
Forests
Forests and forestry
Image Processing and Computer Vision
Image processing systems
International
Localization
Mathematical functions
Pattern Recognition
Pattern Recognition and Graphics
Real time
Sensors
Studies
Three dimensional
Vision
Voting
United Kingdom > UK
Real time
Head pose estimation
3D facial features detection
Random forests
ISSN:0920-5691, 1573-1405
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:We present a random forest-based framework for real time head pose estimation from depth images and extend it to localize a set of facial features in 3D. Our algorithm takes a voting approach, where each patch extracted from the depth image can directly cast a vote for the head pose or each of the facial features. Our system proves capable of handling large rotations, partial occlusions, and the noisy depth data acquired using commercial sensors. Moreover, the algorithm works on each frame independently and achieves real time performance without resorting to parallel computations on a GPU. We present extensive experiments on publicly available, challenging datasets and present a new annotated head pose database recorded using a Microsoft Kinect.
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-012-0549-0