Real-time recognition of medial structures within hand postures through Eigen-space and geometric skeletal shape features

Abstract

Skeletons provide landmark points that preserve implicit strokes or medial structure within a shape for compact representation. Though, hand posture shapes are usually recognized by region or contour representations, some applications may only be interested in the recognition of medial structures within the postures rather than their exact outlines and regions. Proposed work identifies several unique medial structures formed by a set of both one and two-handed postures and demonstrates their pure skeletal recognition in real-time. Existing skeleton-based recognition schemes apply the complex segmental processing on underlying skeleton and rely on contour information which is not suitable for fast recognition of medial structures. Presented work applies intuitive Eigen-space based Principal Components of Symbolic Structure (PCSS) and geometric Equi-Polar Signature (EPS) features to accomplish the recognition task. Both PCV and EPS process the skeleton globally without sections without associating contour information. Recognition accuracy up to 94% is obtained on a 22 posture dataset comprising of 10,560 depth frames with 480 samples for each posture. Depth sensor based acquisition is employed to meet the real-time requirements.