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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 3/2013

01.02.2013

Random Forests for Real Time 3D Face Analysis

verfasst von: Gabriele Fanelli, Matthias Dantone, Juergen Gall, Andrea Fossati, Luc Van Gool

Erschienen in: International Journal of Computer Vision | Ausgabe 3/2013

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Abstract

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.
Vorheriger Artikel Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition
Nächster Artikel Improving Head Movement Tolerance of Cross-Ratio Based Eye Trackers

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Fußnoten
1
Most of the datasets are publicly available at http://​www.​vision.​ee.​ethz.​ch/​datasets.
 
2
Because of the proprietary license for Paysan et al. (2009), we cannot share the above database. The PCA model, however, can be obtained from the University of Basel.
 
3
We used the source code provided by the authors.
 
5
Commercially available: http://​www.​faceshift.​com.
 
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Metadaten
Titel
Random Forests for Real Time 3D Face Analysis
verfasst von
Gabriele Fanelli
Matthias Dantone
Juergen Gall
Andrea Fossati
Luc Van Gool
Publikationsdatum
01.02.2013
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 3/2013
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-012-0549-0

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