Title :
Hand gesture recognition using RGB-D cues
Author :
Lin, Lan ; Cong, Yang ; Tang, Yandong
Abstract :
In this paper, we propose a hand gesture recognition method in the clutter background by fusing the RGB-D cues. Since the hand localization is the key issue, we propose a coarse-to-fine procedure to detect hand accurately, which combines the statistic skin model using color information with depth prior knowledge. By detecting the skin candidate regions on the color image with Gaussian Mixture Model (GMM) skin model, hand region is obtained by compounding the depth information with the assumption that hands are at the closest position to the camera in all skin regions. Then, a new descriptor based on saliency point is used to represent the binary hand properly. A new hand model containing the wrist is proposed and the gesture recognition based on special points is applied. The experiment results demonstrate that our method performs better than NMI and moment based methods with a 96.2% recognition rate.
Keywords :
gesture recognition; image colour analysis; object recognition; skin; GMM skin model; Gaussian mixture model skin model; NMI; RGB-D cues; binary hand properly; camera closest position; clutter background; color image; color information; depth prior knowledge; hand detection; hand gesture recognition; hand localization; hand model; hand region; moment based methods; skin candidate region detection; statistic skin model; Cameras; Color; Gesture recognition; Hidden Markov models; Image color analysis; Skin; Wrist; depth image; hand gesture recognition; saliency points detection; skin model;
Conference_Titel :
Information and Automation (ICIA), 2012 International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4673-2238-6
Electronic_ISBN :
978-1-4673-2236-2
DOI :
10.1109/ICInfA.2012.6246824