Performance Improvement of Phase-Based Correspondence Matching for Palmprint Recognition

The use of phase-based correspondence matching for biometric recognition makes it possible to find corresponding point pairs between images having nonlinear deformation. On the other hand, the optimal recognition performance cannot be exhibited due to simple approaches for matching score calculation and reference point placement. This paper proposes two techniques to improve performance of phase-based correspondence matching for contactless palmprint recognition. First technique analyzes location of corresponding points and defines a new matching score. Second one selects location of reference points suggested by a Difference of Gaussians (DoG) filter. Through a set of experiments using CASIA contactless palmprint database, we demonstrate that the proposed techniques improve performance of phase-based correspondence matching and exhibit good performance compared with conventional palmprint recognition algorithms.