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Facial point detection using boosted regression and graph models (2010)
| Citations: | 56 - 7 self |
BibTeX
@MISC{Valstar10facialpoint,
author = {Michel Valstar and Brais Martinez and Xavier Binefa and Maja Pantic},
title = { Facial point detection using boosted regression and graph models},
year = {2010}
}
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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-feature-based 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.
Keyphrases
graph model facial point detection markov random field head pose geometric-feature-based facial expression analysis unsolved problem support vector regression point location rich naturalistic facial behaviour crucial step algorithm robust facial expression appearance-based feature current state search space art point detector fiducial facial point moderate change fiducial facial point location facial point point detection algorithm
