Real-time Facial Feature Detection using Conditional Regression Forests

Matthias Dantone, Juergen Gall, Gabriele Fanelli, and Luc Van Gool


Although facial feature detection from 2D images is a well-studied field, there is a lack of real-time methods that estimate feature points even on low quality images. Here we propose conditional regression forest for this task. While regression forest learn the relations between facial image patches and the location of feature points from the entire set of faces, conditional regression forest learn the relations conditional to global face properties. In our experiments, we use the head pose as a global property and demonstrate that conditional regression forests outperform regression forests for facial feature detection. We have evaluated the method on the challenging Labeled Faces in the Wild database where close-to-human accuracy is achieved while processing images in real-time.


While a regression forest is trained on the entire training set and applied to all test images, a conditional regression forest consists of multiple forests that are trained on a subset of the training data illustrated by the head poses (colored red, yellow, green). When testing on an image (illustrated by the two faces at the bottom), the head pose is predicted and trees of the various conditional forests (red, yellow, green) are selected to estimate the facial feature points.

Real-time facial feature points estimation. Video ~15MB (AVI)

Source Code/Data

Source Code

If you have questions concerning the source code, please contact Matthias Dantone.


Dantone M., Gall J., Fanelli G., and van Gool L., Real-time Facial Feature Detection using Conditional Regression Forests (PDF), IEEE Conference on Computer Vision and Pattern Recognition (CVPR'12), 2578-2585, 2012. ©IEEE