Static hand gesture recognition with 2 kinect sensors

R. P. Mihail, N. Jacobs, J. Goldsmith

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

12 Scopus citations

Abstract

In this paper, we propose a robust static hand gesture recognition algorithm that makes use of two Kinect sensors. This will be used to control an avatar in a decision aid for rheumatoid arthritis patients who have a difficult time using a standard keyboard and mouse interface. The sensors are placed on the left and right sides of a target sensing area, easily set up in a doctor's office or waiting room. The Kinects provide a rich point cloud, out of which gestures from a known vocabulary are recognized in real time. We use 6 point cloud descriptors simultaneously and employ the majority rule voting scheme to pick a "winner" gesture in real time. We achieve rotation invariance by using part of the forearm as a good indicator of hand orientation and aligning the hand with the world coordinate system origin. We evaluate the performance of the recognition system under various motion and rotation conditions.

Original languageEnglish
Title of host publicationProceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Pages911-917
Number of pages7
StatePublished - 2012
Event2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012 - Las Vegas, NV, United States
Duration: Jul 16 2012Jul 19 2012

Publication series

NameProceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Volume2

Conference

Conference2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Country/TerritoryUnited States
CityLas Vegas, NV
Period7/16/127/19/12

Keywords

  • Gesture recognition
  • Kinect
  • Majority rule
  • Rheumatoid arthritis
  • Voting

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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