DocumentCode :
3316920
Title :
Real-time hand posture recognition based on hand dominant line using kinect
Author :
Yue Wang ; Ruoyu Yang
Author_Institution :
State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China
fYear :
2013
fDate :
15-19 July 2013
Firstpage :
1
Lastpage :
4
Abstract :
Depth sensors like Kinect can provide 2.5D depth data, which shows a extreme difference with regular color data. This paper proposes a simple and novel algorithm to perform hand posture classification using depth sensors. We present the notation of hand dominant line which can be used to make our hand descriptor rotation invariant. The recognition system combines our descriptor and SVM classifier and achieves robust hand pose recognition in real time. In cross validation test where half of dataset is used to train while the other half is used to test, we achieve 97.1% success rate on NTU Dataset and 96.2% on a subset of ASL(American Sign Language) dataset.
Keywords :
image classification; pose estimation; support vector machines; ASL; American Sign Language dataset; Kinect; NTU dataset; SVM classifier; depth sensors; hand descriptor rotation invariant; hand dominant line; hand posture classification; real-time hand posture recognition; robust hand pose recognition; Cameras; Conferences; Gesture recognition; Image segmentation; Real-time systems; Shape; Training; Kinect; hand dominant line; hand posture;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo Workshops (ICMEW), 2013 IEEE International Conference on
Conference_Location :
San Jose, CA
Type :
conf
DOI :
10.1109/ICMEW.2013.6618237
Filename :
6618237
Link To Document :
بازگشت