Open AccessMultimodal random forest based tensor regression
Author(s) -
Kaymak Sertan,
Patras Ioannis
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2013.0320
Subject(s) - random forest , tensor (intrinsic definition) , regression , computer science , artificial intelligence , pattern recognition (psychology) , intensity (physics) , machine learning , data mining , statistics , mathematics , pure mathematics , physics , quantum mechanics
This study presents a method, called random forest based tensor regression, for real‐time head pose estimation using both depth and intensity data. The method builds on random forests and proposes to train and use tensor regressors at each leaf node of the trees of the forest. The tensor regressors are trained using both intensity and depth data and their votes are fused. The proposed method is shown to outperform current state of the art approaches in terms of accuracy when applied to the publicly available Biwi Kinect head pose dataset.