Convolution approach for feature detection in topological skeletons obtained from vascular patterns

In image processing connected structures can be reduced to an abstract binary skeleton. These skeletons are 1-pixel wide structures which retain the topology of the segmented image. They are used for computer vision, edge detection or high level feature extraction for example in biometric systems. In this paper a fast method on how to extract specific feature points from skeletonized structures is presented. The convolution of the skeleton image with a bi-dimensional mask of size M×N enables us to identify arbitrary structures of the mask size in the skeleton. Of special interest are branch and endpoints of the skeletons to get high level features for biometric comparisons. The problem can here be reduced to the following: in an 8-connected skeleton within a 3×3 mask there are 8 structures that correspond to endpoints and 18 to branch points. After applying the convolution, the search for feature points corresponds to finding the 26 different filter response values in the resulting signal. We describe how the convolution approach is applied to biometric vein recognition systems and show that our approach yields a 430% speedup when compared to the crossing number approach used in ANSI/NIST.