Random Forests for Real Time 3D Face Analysis

  • Authors:
  • Gabriele Fanelli;Matthias Dantone;Juergen Gall;Andrea Fossati;Luc Gool

  • Affiliations:
  • Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland 8092;Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland 8092;Perceiving Systems Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany 72076;Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland 8092;Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland 8092 and Department of Electrical Engineering/IBBT, K.U. Leuven, Heverlee, Belgium 3001

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 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.