TY - JOUR
T1 - Continuous Head Pose Estimation Using Manifold Subspace Embedding and Multivariate Regression
AU - Diaz-Chito, Katerine
AU - Del Rincon, Jesus Martinez
AU - Hernandez-Sabate, Aura
AU - Gil, Debora
PY - 2018/3/17
Y1 - 2018/3/17
N2 - © 2013 IEEE. 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 learning-based methods, due to their promising generalization properties shown for face modeling 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 data sets, 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-the-art methods, with angular errors varying between 9° and 17°.
AB - © 2013 IEEE. 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 learning-based methods, due to their promising generalization properties shown for face modeling 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 data sets, 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-the-art methods, with angular errors varying between 9° and 17°.
KW - B-splines
KW - HOG features
KW - Head pose estimation
KW - generalized discriminative common vectors
KW - multiple linear regression
U2 - 10.1109/ACCESS.2018.2817252
DO - 10.1109/ACCESS.2018.2817252
M3 - Article
SN - 2169-3536
VL - 6
SP - 18325
EP - 18334
JO - IEEE Access
JF - IEEE Access
ER -