Abstract
Accurate detection of facial landmarks is very important for many applications like face recognition or analysis. In this paper we describe an efficient detector of facial landmarks based on a cascade of boosted regressors of arbitrary number of levels. We define as many regressors as landmarks and we train them separately. We describe how the training is conducted for the series of regressors by supplying training samples centered on the predictions of the previous levels. We employ gradient boosted regression and evaluate three different kinds of weak elementary regressors, each one based on Haar features: non parametric regressors, simple linear regressors and gradient boosted trees. We discuss trade-offs between the number of levels and the number of weak regressors for optimal detection speed. Experiments performed on three datasets suggest that our approach is competitive compared to state-of-the art systems regarding precision, speed as well as stability of the prediction on video streams.
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Notes
- 1.
We did some tests with an implementation of [8], but it gave results very different to what was reported in the paper, thus we do not present them.
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This work was partially funded by the QUAERO project supported by OSEO and by the European integrated project AXES.
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Chevallier, L., Vigouroux, JR., Goguey, A., Ozerov, A. (2014). Facial Landmarks Localization Estimation by Cascaded Boosted Regression. In: Battiato, S., Coquillart, S., Laramee, R., Kerren, A., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics -- Theory and Applications. VISIGRAPP 2013. Communications in Computer and Information Science, vol 458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44911-0_7
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