Enhanced hand part classification from a single depth image using random decision forests
Hand pose recognition has received increasing attention in an area of human–computer interaction. With the recent spread of many low-cost three-dimensional (3D) cameras, research into understanding more natural gestures has increased. In this study, the authors present a method for hand part classification and joint estimation from a single depth image. They apply random decision forests (RDFs) for hand part classification. Foreground pixels in the hand image are estimated by RDF. Then hand joints are estimated based on the classified hand parts. They suggest a robust feature extraction method for per-pixel classification, which enhances the accuracy of hand part classification. They also propose a tree selection algorithm using legacy trained RDF to classify unseen test data. Selecting trees using the proposed method show better performance than using all the trees as in conventional method. Depth images and label images synthesised by 3D hand mesh model were used for training forests and algorithm verification. The authors’ experiments show that the enhanced algorithm outperforms the state-of-the-art method in accuracy.