Combining features for distorted fingerprint matching

Extracting and fusing discriminative features in fingerprint matching, especially in distorted fingerprint matching, is a challenging task. In this paper, we introduce two novel features to deal with nonlinear distortion in fingerprints. One is finger placement direction which is extracted from fingerprint foreground and the other is ridge compatibility which is determined by the singular values of the affine matrix estimated by some matched minutiae and their associated ridges. Both of them are fixed-length and easy to be incorporated into matching score. In order to improve the matching performance, we combine these two features with orientation descriptor and local minutiae structure, which are used to measure minutiae similarity, to achieve fingerprint matching. In addition, we represent minutiae set as a graph and use graph connect component and iterative robust least square (IRLS) to detect creases and remove spurious minutiae close to creases. Experimental results on FVC2004 DB1 and DB3 demonstrate that the proposed algorithm could obtain promising results. The equal error rates (EER) are 3.35% and 1.49% on DB1 and DB3, respectively.