Robust and accurate shape model fitting using random forest regression voting

Authors:
Tim F. Cootes;Mircea C. Ionita;Claudia Lindner;Patrick Sauer
Affiliations:
The University of Manchester, UK;The University of Manchester, UK;The University of Manchester, UK;The University of Manchester, UK
Venue:
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Year:
2012

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Abstract

A widely used approach for locating points on deformable objects is to generate feature response images for each point, then to fit a shape model to the response images. We demonstrate that Random Forest regression can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position. We show this leads to fast and accurate matching when combined with a statistical shape model. We evaluate the technique in detail, and compare with a range of commonly used alternatives on several different datasets. We show that the random forest regression method is significantly faster and more accurate than equivalent discriminative, or boosted regression based methods trained on the same data.