Head pose estimation with combined 2D SIFT and 3D HOG features

Bingjie Wang, Wei Liang, Yucheng Wang, Yan Liang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

34 Citations (Scopus)

Abstract

In this paper, an approach is presented to estimate the 3D position and orientation of head from RGB and depth images captured by a commercial sensor Kinect. We use 2D Scale-invariant feature transform (SIFT) features together with 3D histogram of oriented gradients (HOG) features which are extracted in a pair of RGB and depth images captured synchronously, named SIFT-HOG features, to improve the robustness and accuracy of head pose estimation. We apply random forests to formulate pose estimation as a regression problem, due to their power for handling large training data and the high mapping speed. And then the mean-shift method is employed to refine the result obtained by the random forests. The experiment results demonstrate that our approach of head pose estimation is efficient.

Original languageEnglish
Title of host publicationProceedings - 2013 7th International Conference on Image and Graphics, ICIG 2013
Pages650-655
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 7th International Conference on Image and Graphics, ICIG 2013 - Qingdao, Shandong, China
Duration: 26 Jul 201328 Jul 2013

Publication series

NameProceedings - 2013 7th International Conference on Image and Graphics, ICIG 2013

Conference

Conference2013 7th International Conference on Image and Graphics, ICIG 2013
Country/TerritoryChina
CityQingdao, Shandong
Period26/07/1328/07/13

Keywords

  • HOG
  • Head Pose Estimation
  • Random Forests
  • SIFT

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