Real-Time Hand Finger Motion Capturing Using Regression Forest

Abstract

This paper proposes a real-time hand finger motion capturing method using Kinect. It consists of three modules: hand region segmentation, feature points extraction, and joint angle estimation. The first module extracts the hand region from the depth image. The second module applies a pixel classifier to segment the hand region into eight characteristic sub-regions and the residual sub-region. The centroid of each characteristic sub-region is extracted as the feature point. The third module converts these feature points to the feature vector for finger joint angle estimation by using the regression forest. The estimation process has both the speed and precision advantages and it can also deal with the hand finger motion parameter of novel hand gesture. The experimental results show that our method can capture the hand finger motion parameters of global in-plane hand rotation with sufficient estimation accuracy.