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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 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.

Author(s): Fanelli, G. and Dantone, M. and Gall, J. and Fossati, A. and van Gool, L.
Journal: International Journal of Computer Vision
Volume: 101
Number (issue): 3
Pages: 437--458
Year: 2013
Publisher: Springer

Department(s): Perceiving Systems
Research Project(s): Faces
Bibtex Type: Article (article)
Paper Type: Journal

DOI: http://dx.doi.org/10.1007/s11263-012-0549-0

Links: data and code
publisher's site
Attachments: pdf


  title = {Random Forests for Real Time {3D} Face Analysis},
  author = {Fanelli, G. and Dantone, M. and Gall, J. and Fossati, A. and van Gool, L.},
  journal = {International Journal of Computer Vision},
  volume = {101},
  number = {3},
  pages = {437--458},
  publisher = {Springer},
  year = {2013}