Face Alignment Via Component-Based Discriminative Search

  • Lin Liang
  • Rong Xiao
  • Fang Wen
  • Jian Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)


In this paper, we propose a component-based discriminative approach for face alignment without requiring initialization. Unlike many approaches which locally optimize in a small range, our approach searches the face shape in a large range at the component level by a discriminative search algorithm. Specifically, a set of direction classifiers guide the search of the configurations of facial components among multiple detected modes of facial components. The direction classifiers are learned using a large number of aligned local patches and misaligned local patches from the training data. The discriminative search is extremely effective and able to find very good alignment results only in a few (2~3) search iterations. As the new approach gives excellent alignment results on the commonly used datasets (e.g., AR [18], FERET [21]) created under-controlled conditions, we evaluate our approach on a more challenging dataset containing over 1,700 well-labeled facial images with a large range of variations in pose, lighting, expression, and background. The experimental results show the superiority of our approach on both accuracy and efficiency.


Facial Image Image Patch Initial Shape Local Patch Face Shape 
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 2008

Authors and Affiliations

  • Lin Liang
    • 1
  • Rong Xiao
    • 1
  • Fang Wen
    • 1
  • Jian Sun
    • 1
  1. 1.Microsoft Research AsiaBeijingChina

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