One Millisecond Face Alignment with an Ensemble of Regression Trees

In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014) [Paper]

Abstract 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 efficient feature selection. Different regularization strategies and its importance to combat overfitting are also investigated. 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.


An implementation of the algorithm is available in dlib C++ library.
Alternatively DEST includes an implementation with some additional tracking features.



* The reported results are the average landmark distance from the ground-truth landmarks normalized by the inter-ocular distance. In all the experiments its is assumed that the bounding box of the face is given. In practice we used OpenCV for detecting the faces. For the cases that the face detector failed, we generated a bounding box with at least 80% overlap with the ground truth bounding box.

** Our reimplementation of ESR performed significantly worse than the result reported in their original paper (~0.043 as opposed to 0.034).

*** We only used the AFW subset in addition to the training set of LFPW, and HELEN for training. For test we used IBUG set with test of LFPW and HELEN.

STASM, CompASM: Interactive Facial Feature Localization.
EXEM: Localizing Parts of Faces Using a Consensus of Exemplars.
RCPR: Robust Face Landmark Estimation Under Occlusion
SDM: Supervised Descent Method and its Applications to Face Alignment.
ESR: Face Alignment by Explicit Shape Regression.