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Real Time 3D Face Alignment with Random Forests-based Active Appearance Models

G. Fanelli and M. Dantone and L. Van Gool
Automatic Face and Gesture Recognition
April 2013

Abstract

Many desirable applications dealing with automatic face analysis rely on robust facial feature localization. While extensive research has been carried out on standard 2D imagery, recent technological advances made the acquisition of 3D data both accurate and affordable, opening new ways to more accurate and robust algorithms. We present a model- based approach to real time face alignment, fitting a 3D model to depth and intensity images of unseen expressive faces. We use random regression forests to drive the fitting in an Active Appearance Model framework. We thoroughly evaluated the proposed approach on publicly available datasets and show how adding the depth channel boosts the robustness and accuracy of the algorithm.


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@InProceedings{eth_biwi_01019,
  author = {G. Fanelli and M. Dantone and L. Van Gool},
  title = {Real Time 3D Face Alignment with Random Forests-based Active Appearance Models},
  booktitle = {Automatic Face and Gesture Recognition},
  year = {2013},
  month = {April},
  keywords = {}
}