DocumentCode
573997
Title
Multi-scenario gesture recognition using Kinect
Author
Yi Li
Author_Institution
Comput. Eng. & Comput. Sci., Univ. of Louisville, Louisville, KY, USA
fYear
2012
fDate
July 30 2012-Aug. 1 2012
Firstpage
126
Lastpage
130
Abstract
Hand gesture recognition (HGR) is an important research topic because some situations require silent communication with sign languages. Computational HGR systems assist silent communication, and help people learn a sign language. In this article, a novel method for contact-less HGR using Microsoft Kinect for Xbox is described, and a real-time HGR system is implemented. The system is able to detect the presence of gestures, to identify fingers, and to recognize the meanings of 18 gestures in two pre-defined gesture scenarios: Popular Gesture and the Numbers. The accuracy of the HGR system for Popular Gesture scenario is from 84% to 99% if a single hand performs the gestures, and from 90% to 100% if both hands perform the same gesture at the same time. The accuracy of the HGR system for the Numbers scenario is from 74% to 100% for single-hand gestures. Because the depth sensor of Kinect is an infrared camera, the lighting conditions, signers´ skin colors and clothing, and background have little impact on the performance of this system. The accuracy and the robustness make this system a versatile component that can be integrated in a variety of applications in daily life.
Keywords
cameras; gesture recognition; human computer interaction; image colour analysis; infrared detectors; learning (artificial intelligence); real-time systems; Kinect depth sensor; Microsoft Kinect; Xbox; both hand gesture recognition; computational HGR systems; contact-less HGR; infrared camera; lighting conditions; multiscenario gesture recognition; number gesture scenario; popular gesture scenario; real-time HGR system; sign language learning; signer clothing; signer skin colors; silent communication; single hand gesture recognition; Accuracy; Cameras; Gesture recognition; Handicapped aids; Thumb; Vectors; Human-computer interaction; Kinect; finger identification; hand gesture recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Games (CGAMES), 2012 17th International Conference on
Conference_Location
Louisville, KY
Print_ISBN
978-1-4673-1120-5
Type
conf
DOI
10.1109/CGames.2012.6314563
Filename
6314563
Link To Document