Facial Point Detection using Boosted Regression and Graph Models


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Valstar, Michel and Martinez, Brais and Binefa, Xavier and Pantic, Maja (2010) Facial Point Detection using Boosted Regression and Graph Models. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, 13-18 June 2010, San Francisco, USA.

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Abstract:Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-featurebased facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a point’s location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors.
Item Type:Conference or Workshop Item
Copyright:© 2010 IEEE
Faculty:
Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:
Link to this item:http://purl.utwente.nl/publications/75978
Official URL:http://dx.doi.org/10.1109/CVPR.2010.5539996
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