@Article{KaterineDiaz2018,
author="Katerine Diaz
and Jesus Martinez del Rincon
and Aura Hernandez-Sabate
and Debora Gil",
title="Continuous head pose estimation using manifold subspace embedding and multivariate regression",
journal="IEEE Access",
year="2018",
volume="6",
pages="18325--18334",
optkeywords="Head Pose estimation",
optkeywords="HOG features",
optkeywords="Generalized Discriminative Common Vectors",
optkeywords="B-splines",
optkeywords="Multiple linear regression",
abstract="In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees.",
optnote="ADAS; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3091), last updated on Thu, 15 Sep 2022 10:24:03 +0200",
issn="2169-3536",
doi="10.1109/ACCESS.2018.2817252",
file=":http://refbase.cvc.uab.es/files/DMH2018b.pdf:PDF"
}