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Face Alignment by Explicit Shape Regression
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DOI：10.1007/s11263-013-0667-3.png)
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Abstract
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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 facial 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 into the regressor in a cascaded learning framework and applied from coarse to fine during the test, without using a fixed parametric shape model as in most previous methods. To make the regression more effective and efficient, we design a two-level boosted regression, shape indexed features and a correlation-based feature selection method. This combination enables us to learn accurate models from large training data in a short time (20 min for 2,000 training images), and run regression extremely fast in test (15 ms 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.
Keywords:
Face alignment
Shape indexed feature
Correlation based feature selection
Non-parametric shape constraint
Tow-level boosted regression
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