Hand gesture recognition from kinect depth images - Yeloglu, Zeynep; Akbulut, Yaman; Budak, Umit; Sengur, Abdulkadir (Institute of Electrical and Electronics Engineers Inc., 2015)
In this study, hand gesture classification method based on depth images is proposed. The proposed method is composed of thresholding, feature extraction, feature selection and classification stages. Hand segmentation on the depth images is carried out based on interval thresholding, curvature scale space is used for feature extraction, sequential feature selection is considered for feature selection and K-Nearest Neighbor method is used for classification. The performance evaluation of the proposed method is tested on 1000 sampled dataset. Experimental works show that the hand gestures which indicate from 0 to 9 can be recognized with 98.33 % accuracy. This accuracy rate is about 4% better than the compared method. © 2015 IEEE.
Kinect Derinlik Imgelerinden El Hareketi Tanima
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- Malatya -- 113052
Keywords: Classification; Curvature scale space; Depth images; Hand gestures