Stable and real-time hand gesture recognition based on RGB-D data

Bo Liu; Guijin Wang; Xinghao Chen; Bei He

doi:10.1117/12.2038084

19 December 2013 Stable and real-time hand gesture recognition based on RGB-D data

Bo Liu, Guijin Wang, Xinghao Chen, Bei He

Proceedings Volume 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology; 90450L (2013) https://doi.org/10.1117/12.2038084
Event: International Conference on Optical Instruments and Technology (OIT2013), 2013, Beijing, China

Abstract

Hand gesture recognition has attracted more interest in computer vision and image processing recently. Recent works for hand gesture recognition confronted 2 major problems. The former one is how to detect and extract the hand region from color-confusing background objects. The latter one is the expensive computational cost by considering the kinematic hand model with up to 27 degrees of freedom. This paper proposes a stable and real-time static hand gesture recognition system. Our contributions are listed as follows. First, to deal with color-confusing background objects, we take the RGB-D (RGB-Depth) information into account, where foreground and background objects can be segmented well. Additionally, a coarse-to-fine model is proposed, which utilizes the skin color and helps us extract the hand region robustly and accurately. Second, considering the principal direction of hand region is random, we introduce the principal component analysis (PCA) algorithm to estimate and then compensate the direction. Finally, to avoid the expensive computational cost of traditional optimization, we design a fingertip filter and detect extended fingers via calculating their distances to palm center and curvature easily. Then the number of extended fingers will be reported, which corresponds to the recognition result. Experiments have verified the stability and high-speed of our algorithm. On the data set captured by the depth camera, our algorithm recognizes the 6 pre-defined static hand gestures robustly with average accuracy about 98.0%. Furthermore, the average computational time for each image (with the resolution 640×480) is 37ms, which can be extended to many real-time applications.

Citation Download Citation

Bo Liu, Guijin Wang, Xinghao Chen, and Bei He "Stable and real-time hand gesture recognition based on RGB-D data", Proc. SPIE 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 90450L (19 December 2013); https://doi.org/10.1117/12.2038084

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available