Articulated Hand Tracking by PCA-ICA Approach (Kato 2006)

Summary:
The method proposed to track finger and hand motions in this paper begins by performing principle component analysis to reduce the number of feature dimensions that are considered. Then independent component analysis is performed on the lower dimensional result to get intrinsic feature vectors. The ICA technique finds a linear, non-orthogonal coordinate system in multivariate data with the goal of performing a linear transformation which makes the resulting variables as statistically independent as possible.
A hand was modeled in OpenGL and projected onto an image captured of a real hand to test the PCA-ICA method. The report of the results only says the effectiveness of the method used to track a hand in real image sequences was demonstrated. There were not any numerical statistics reported in the results.

Discussion:
The PCA-ICA method considers only finger bentness, not hand position or orientation. Recognizing hand gestures that rely on that kind of data could not solely depend n the PCA-ICA approach.
The pictures of hand motion based on ICA basis vectors look better than the pictures of hand motion based on PCA basis vectors. I think this may be due to the fact that five vectors were considered. What would ICA look like if some number other than five was used? I don't know if there would be a good mapping of ICA basis vectors to hand renderings for a number of vectors other than five (one for each finger).
I think one of the insights of this paper was its use of knowledge from a different field. The number of dimensions used in component analysis can be reduced by considering important observations from the field of biomechanics. In particular the bend of an end joint of a finger can typically be described as two thirds the bend of the next joint's bend along the same finger.