Hand Pointing Detection Using Live Histogram Template of Forehead Skin
This addresses hand pointing detection for applications like virtual reality and smart home control, but it is incremental as it builds on existing methods like background subtraction and histogram templates.
The paper tackles hand pointing detection in 2D space using a live video from a common webcam, achieving 94% true positive and 85% true negative rates.
Hand pointing detection has multiple applications in many fields such as virtual reality and control devices in smart homes. In this paper, we proposed a novel approach to detect pointing vector in 2D space of a room. After background subtraction, face and forehead is detected. In the second step, forehead skin H-S plane histograms in HSV space is calculated. By using these histogram templates of users skin, and back projection method, skin areas are detected. The contours of hand are extracted using Freeman chain code algorithm. Next step is finding fingertips. Points in hand contour which are candidates for the fingertip can be found in convex defects of convex hull and contour. We introduced a novel method for finding the fingertip based on the special points on the contour and their relationships. Our approach detects hand-pointing vectors in live video from a common webcam with 94%TP and 85%TN.