Anthropometric human hand models for tracking

Gestures are a natural form of interaction for humans, lending themselves to applications ranging from virtual reality systems to assisted communication. Accordingly, vision-based hand gesture recognition has shown promise as a computer interface medium, but the complexity and high dimensionality of hand shapes poses a barrier for effective modeling. While previous work have explored methods for extracting a minimal feature set, we propose applying dimension reduction techniques to compactly represent the natural variation found in human hands. From an initial set of 25 feature dimensions, we discover an underlying embedding of 4-6 modes. This low-dimensional manifold is then used to dynamically reconstruct with a high degree of accuracy the higher dimensional parameters, which are integrated with a tracking module of a real-time gesture recognition system.