Face alignment by explicit shape regression

  • Cao X
  • Wei Y
  • Wen F
 et al. 
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Abstract

We present a very efficient, highly accurate, “Explicit Shape Regression” approach for face alignment. Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole fa- cial shape (a set of facial landmarks) from the image and explicitly minimize the alignment errors over the training data. The inherent shape constraint is naturally encoded in- to the regressor in a cascaded learning framework and ap- plied from coarse to fine during the test, without using a fixed parametric shape model as in most previousmethods. To make the regression more effective and efficient, we design a two-level boosted regression, shape-indexed fea- tures and a correlation-based feature selectionmethod. This combination enables us to learn accuratemodels fromlarge training data in a short time (20 minutes for 2,000 training images), and run regression extremely fast in test (15 m- s for a 87 landmarks shape). Experiments on challenging data show that our approach significantly outperforms the state-of-the-art in terms of both accuracy and efficiency

Author-supplied keywords

  • Correlation based feature selection
  • Face alignment
  • Non-parametric shape constraint
  • Shape indexed feature
  • Tow-level boosted regression

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Authors

  • Xudong Cao

  • Yichen Wei

  • Fang Wen

  • Jian Sun

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