Articulated Hand Tracking by PCA-ICA Approach (Kato, et al – 2006)

Current Mood: studious

Blogs I Commented On:

• Josh Johnston
• Pankaj Rajan
• Aaron Wolin

Summary:
This paper focuses on hand tracking using a PCA-ICA approach. To do so, the authors first model the human with OpenGL as spheres, columns, and a rectangular parallelepiped. Hand motion data is capture with a data glove by capturing all combinations of open and closed fingers so that angles for 20 joints were measured. These measurements were divided into 100 instances to obtain a 2000-dimensional hand motion row vector. PCA is then used to find a smaller set of variables with less redundancy, measured by correlations between data elements using Singular Value Decomposition. From their approach, the authors first use PCA to reduce dimensionality, and then perform ICA on the low-dimensional PCA subspace to extract feature vectors. For ICA, the authors use a neural learning algorithm to maximize the joint entropy by using stochastic gradient ascent. The ICA-based model thus can represent a hand pose by five independent parameters corresponding to a particular finger at a particular time instant. From the PCA-ISA approach, PCA basis vectors represent global hand motion with mostly unfeasible hand motions, where ICA basis vectors represent particular finger motion. Particle filtering is then used for tracking hands by first generating samples where the hand pose is determined by five parameters (corresponding to each finger) from the ICA-based model, and then by using an observation model for employing edge and silhouette information to evaluate their hypothesis.

Discussion:
If I pretend what the paper was talking about then I will say that I found it intriguing that they combined the strengths of PCA and ICA to come up with what appears to be a viable hand tracking system, in that PCA’s limitations were overcome by ICA to model the hand for tracking purposes. It’s kind of hard to judge the merits of this paper though based on scant results, but the images provided at the end of the paper in less-than-ideal environments. I wish it had working actual results though (online video link is dead).