  Authentication of individuals via palmprint based biometric system is
becoming very popular due to its reliability as it contains unique and stable
features. In this paper, we present a novel approach for palmprint recognition
and its representation. To extract the palm lines, local thresholding technique
Niblack binarization algorithm is adopted. The endpoints of these lines are
determined and a connection is created among them using the Delaunay
triangulation thereby generating a distinct topological structure of each
palmprint. Next, we extract different geometric as well as quantitative
features from the triangles of the Delaunay triangulation that assist in
identifying different individuals. To ensure that the proposed approach is
invariant to rotation and scaling, features were made relative to topological
and geometrical structure of the palmprint. The similarity of the two
palmprints is computed using the weighted sum approach and compared with the
k-nearest neighbor. The experimental results obtained reflect the effectiveness
of the proposed approach to discriminate between different palmprint images and
thus achieved a recognition rate of 90% over large databases.
