“One Millisecond Face Alignment with an Ensemble of Regression Trees”

This paper addresses the problem of Face Alignment for
a single image. We show how an ensemble of regression
trees can be used to estimate the face’s landmark positions
directly from a sparse subset of pixel intensities, achieving
super-realtime performance with high quality predictions.
We present a general framework based on gradient boosting
for learning an ensemble of regression trees that optimizes
the sum of square error loss and naturally handles missing
or partially labelled data. We show how using appropriate
priors exploiting the structure of image data helps with ef-
ficient feature selection. Different regularization strategies
and its importance to combat overfitting are also investi-
gated. In addition, we analyse the effect of the quantity of
training data on the accuracy of the predictions and explore
the effect of data augmentation using synthesized data.

From One Millisecond Face Alignment with an Ensemble of Regression Trees Vahid Kazemi and Josephine Sullivan; KTH, Royal Institute of Technology
Computer Vision and Active Perception Lab
Teknikringen 14, Stockholm, Sweden


Cited by this blog post which uses the technique, among other things to match and then project one face onto another.

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