Automatic landmarking of 2D images
Facial landmark detection, or localisation, is an essential
preprocessing step in any automatic face analysis system. A 2D FLD
algorithm is usually applied to the content of a face bounding box
output by a face detector, and attempts to locate the positions of a set
of pre-defined landmarks (key points) such as eyebrows, eye centres,
nose tip or mouth corners of the facial parts in a 2D image.
We have made three contributions to the field:
- Cascaded collaborative regression trained on a hybrid dataset :
A large amount of training data is usually crucial for successful
supervised learning. However, the task of providing training samples
is often time-consuming, involving a considerable amount of tedious
manual work. To augment our training set, we used a 3D morphable face
model to generate synthesised faces in supervised cascaded regression
training. These were used in conjunction with a smaller set of natural images.
Initially the training uses mainly the synthetic data, as this can
model the gross variations between the various poses. As the training
proceeds, progressively more of the natural images are incorporated,
as these can model finer detail. The results indicated the benefits
of this approach over the state of the art.
- An adaptive random cascaded regression copse :
In light of the difficulties posed by scale variations of image
photography in practical scenarios, we developed an adaptive scheme
for scale-invariant shape-indexed feature extraction and shape update
in cascaded-regression-based FLD. Furthermore, to enhance the
generalization capacity of this framework, we present a random CR
"copse", fusing information by a simple voting approach. Experimental
results on several well-known face databases show the benefits of the
- Tensor-based AAM with missing values : The use of tensor
algebra requires a complete training dataset that consists of the
training samples under all different variations. It is usually very
difficult to collect such a dataset in practice, especially when we
have many variation types. We developed two solutions to build a
unified T-AAM from an incomplete training set with missing training
examples using two state-of-the-art tensor augmentation algorithms,
M²SA and CP-WOPT. The application of this method to landmark
detection validated the approach.
- Feng, Hu, Kittler, Christmas, Wu: Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting. IEEE Transactions on Image Processing, Vol.24(11), 2015, pp:3425-3440.
- Feng, Huber, Kittler, Christmas and Wu: Random Cascaded-Regression Copse for Robust Facial Landmark Detection.
IEEE Signal Processing Letters, Vol.22(1), 2015, pp:76-80.
- Feng, Kittler, Christmas, Wu and Pfeiffer: Automatic face annotation by multilinear AAM with Missing Values.
ICPR, 2012, pp: 2586-2589
Zhenhua Feng, Bill Christmas
Last modified: Thu Jul 16 11:55:43 BST 2015