ACCV 2012: Computer Vision – ACCV 2012 pp 667-679 | Cite as

Face Parts Localization Using Structured-Output Regression Forests

  • Heng Yang
  • Ioannis Patras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7725)

Abstract

In this paper, we propose a method for face parts localization called Structured-Output Regression Forests (SO-RF). We assume that the spatial graph of face parts structure can be partitioned into star graphs associated with individual parts. At each leaf, a regression model for an individual part as well as an interdependency model between parts in the star graph is learned. During testing, individual part positions are determined by the product of two voting maps, corresponding to two different models. The part regression model captures local feature evidence while the interdependency model captures the structure configuration. Our method has shown state of the art results on the publicly available BioID dataset and competitive results on a more challenging dataset, namely Labeled Face Parts in the Wild.

Keywords

Leaf Node Training Image Individual Part Star Graph Active Appearance Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Heng Yang
    • 1
  • Ioannis Patras
    • 1
  1. 1.School of EECSQueen Mary University of LondonUK

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