Tracking hand rotation and various grasping gestures from an IR camera using extended cylindrical manifold embedding

This paper presents a new approach for tracking hand rotation and various grasping gestures through an infrared camera. For the complexity and ambiguity of an observed hand shape, it is difficult to simultaneously estimate hand configuration and orientation from a silhouette image of a grasping hand gesture. This paper proposes a dynamic shape model for hand grasping gestures using cylindrical manifold embedding to analyze variations of hand shape in different hand configurations between two key hand poses and in simultaneous circular view change by hand rotation. An arbitrary hand shape between two key hand poses from any view can be generated using a cylindrical manifold embedding point after learning nonlinear generative models from the embedding space to the corresponding hand shape observed. The cylindrical manifold embedding model is extended to various grasping gestures by decomposing multiple cylindrical manifold embeddings through grasping style analysis. Grasping hand gestures with simultaneous hand rotation are tracked using particle filters on the manifold space with grasping style estimation. Experimental results for synthetic and real data indicate that the proposed model can accurately track various grasping gestures with hand rotation. The proposed approach may be applied to advanced user interfaces in dark environments by using images beyond the visible spectrum.