Articulated hand tracking by PCA-ICA approach

This paper introduces a new representation of hand motions for tracking and recognizing hand-finger gestures in an image sequence. A human hand has 15 joints 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 to represent 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 hand-finger gestures in an image sequence. This paper demonstrates the effectiveness of our method by tracking hands in real image sequences