Hand features extractor using hand contour – a case study

Abstract

Hand gesture recognition is an important topic in natural user interfaces (NUI). Hand features extraction is the first step for hand gesture recognition. This work proposes a novel real time method for hand features recognition. In our framework we use three cameras and the hand region is extracted with the background subtraction method. Features like arm angle and fingers positions are calculated using Y variations in the vertical contour image. Wrist detection is obtained by calculating the bigger distance from a base line and the hand contour, giving the main features for the hand gesture recognition. Experiments on our own data-set of about 1800 images show that our method performs well and is highly efficient.

Cognitive Visual Tracking of Hand Gestures in Real-Time RGB Videos

“…They proposed three camera setups to recognize bimanual gestures in HGR. Similarly, Bautista A.G. et al [18] used three cameras in their system to avoid complex background and illumination. Marin G. et al [19] suggested combining Kinect data with Leap motion camera data to exploit the complementary characteristics of both the cameras.…”