Hand shape recognition using distance transform and shape decomposition

Abstract

Hand shape is a natural and human-friendly interface for human-computer interaction. This paper proposes a realtime and 2D vision-based hand shape recognition method. The method is robust to hand pose changes because the hand pose is neutralized after recognizing a hand pose using distance transform, principal component analysis (PCA), and histogram analysis. Also, the context-based recognition method using shape decomposition can effectively recognize tiny changes of fingers. The method worked at 44.8 fps and had a recognition rate of 83% on average in the experiment with 800 images including 5 hand shapes and 16 hand poses.