Articulated Hand Motion Tracking Using ICA-based Motion Analysis and Particle Filtering

This paper introduces a new representation of hand motions for tracking and recognizing hand-finger gestures in an image sequence. A human hand has many joints, for example our hand model has 15, and its high dimensionality makes it difficult to model hand motions. To make things easier, it is important to represent a hand motion in a low dimensional space. Principle component analysis (PCA) has been proposed to reduce the dimensionality. However, the PCA basis vectors only represent global features, which are not optimal for representing intrinsic features. This paper proposes an efficient representation of hand motions by independent component analysis (ICA). The ICA basis vectors represent local features, each of which corresponds to the motion of a particular finger. This representation is more efficient in modeling hand motions for tracking and recognizing handfinger gestures in an image sequence. We will demonstrate the effectiveness of the method by tracking a hand in real image sequences.