  We present a hierarchical regression framework for estimating hand joint
positions from single depth images based on local surface normals. The
hierarchical regression follows the tree structured topology of hand from wrist
to finger tips. We propose a conditional regression forest, i.e., the Frame
Conditioned Regression Forest (FCRF) which uses a new normal difference
feature. At each stage of the regression, the frame of reference is established
from either the local surface normal or previously estimated hand joints. By
making the regression with respect to the local frame, the pose estimation is
more robust to rigid transformations. We also introduce a new efficient
approximation to estimate surface normals. We verify the effectiveness of our
method by conducting experiments on two challenging real-world datasets and
show consistent improvements over previous discriminative pose estimation
methods.
