Robust Face Alignment Based on Hierarchical Classifier Network

  • Li Zhang
  • Haizhou Ai
  • Shihong Lao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3979)


Robust face alignment is crucial for many face processing applications. As face detection only gives a rough estimation of face region, one important problem is how to align facial shapes starting from this rough estimation, especially on face images with expression and pose changes. We propose a novel method of face alignment by building a hierarchical classifier network, connecting face detection and face alignment into a smooth coarse-to-fine procedure. Classifiers are trained to recognize feature textures in different scales from entire face to local patterns. A multi-layer structure is employed to organize the classifiers, which begins with one classifier at the first layer and gradually refines the localization of feature points by more classifiers in the following layers. A Bayesian framework is configured for the inference of the feature points between the layers. The boosted classifiers detects facial features discriminately from its local neighborhood, while the inference between the layers constrains the searching space. Extensive experiments are reported to show its accuracy and robustness.


Feature Point Face Image Facial Feature Principle Component Analysis Face Detection 
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 2006

Authors and Affiliations

  • Li Zhang
    • 1
  • Haizhou Ai
    • 1
  • Shihong Lao
    • 2
  1. 1.Department of Computer ScienceTsinghua UniversityBeijingChina
  2. 2.Sensing and Control Technology LabOmron CorporationKyotoJapan

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