Hand Gesture Recognition From Kinect Depth Images

Abstract

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.