Hand Gesture Recognition Based on the Parallel Edge Finger Feature and Angular Projection

Abstract

In this paper, a novel high-level hand feature extraction method is proposed by the aid of finger parallel edge feature and angular projection. The finger is modelled as a cylindrical object and the finger images can be extracted from the convolution with a specific operator as salient hand edge images. Hand center, hand orientation and wrist location are then determined via the analysis of finger image and hand silhouette. The angular projection of the finger images with origin point on the wrist is calculated, and five fingers are located by analyzing the angular projection. The proposed algorithm can detect extensional fingers as well as flexional fingers. It is robust to the hand rotation, side movements of fingers and disturbance from the arm. Experimental results demonstrate that the proposed method can directly estimate simple hand poses in real-time.