IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
Generic vs. person specific active appearance models
Image and Vision Computing
Coupled Prediction Classification for Robust Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regression forests for efficient anatomy detection and localization in CT studies
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Hough Forests for Object Detection, Tracking, and Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real time head pose estimation with random regression forests
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Localizing parts of faces using a consensus of exemplars
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Face detection, pose estimation, and landmark localization in the wild
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Face alignment by Explicit Shape Regression
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Efficient regression of general-activity human poses from depth images
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Facial landmark detection in uncontrolled conditions
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
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In this paper, we propose a method for face parts localization called Structured-Output Regression Forests (SO-RF). We assume that the spatial graph of face parts structure can be partitioned into star graphs associated with individual parts. At each leaf, a regression model for an individual part as well as an interdependency model between parts in the star graph is learned. During testing, individual part positions are determined by the product of two voting maps, corresponding to two different models. The part regression model captures local feature evidence while the interdependency model captures the structure configuration. Our method has shown state of the art results on the publicly available BioID dataset and competitive results on a more challenging dataset, namely Labeled Face Parts in the Wild.