Facial Landmarks Localization Estimation by Cascaded Boosted Regression

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.