Rapid hand posture recognition using Adaptive Histogram Template of Skin and hand edge contour

Abstract

In this paper, we propose a real-time vision-based hand posture recognition approach, based on appearance-based features of hand. Our approach has three main steps: hand segmentation, feature extraction and posture recognition. For the hand segmentation, we introduce “Adaptive Histogram Template of Skin” which tries to extract histogram of the subject hand by sampling its color and texture. With this template, we can use back projection method to find skin color areas in an image. In the feature extraction step, we extract global hand's features using hand's edge contour, and hand's edge convex hull. The hand can be classified into one of the ten posture classes in the recognition step. Each posture class has a representative template which is used as reference for comparing to subject hand features. This approach is simple and fast enough to provide real-time recognition.