Real Time Fingertip Detection with Kinect Depth Image Sequences

Abstract

Gesture recognition has been a research focus with the popularity of depth sensing device. In this paper, we propose a new fingertip detection method based on a novel definition of fingers. This method consists of two steps. Firstly, finger bases are detected and estimated as prior information. Secondly, the finger regions and fingertips are located. In the second module, the point cloud of hand is represented as a graph to obtain all geodesic paths originated from palm center. If one path travels through a finger base, then its terminal point is defined as a finger point. The fingertips are determined within these finger points by utilizing geodesic distances. To our knowledge, such definition has never been applied in gesture recognition before and its performance surpasses the definition of geodesic maxima. Experimental results demonstrates the effectiveness of our method even when hand is not parallel to camera. Compared with state-of-the-art approach, our method shows much less error.

Hand Segmentation and Fingertip Tracking from Depth Camera Images Using Deep Convolutional Neural Network and Multi-task SegNet

“…The benefit of this hypothesis is that such simple computer vision techniques can be employed as background subtraction, etc. The other approach for hand image processing, which is to use RGB-D images, was applied in [17,20]. In lieu of using a hand detector for RGB images, the authors located fingertips by assuming that the hand must be at the shortest distance from the camera with the given depth information.…”