Dense Fingerprint Registration via Displacement Regression Network Dense registration of fingerprints provides pixelwise correspondences between two fingerprints, which is beneficial for fingerprint mosaicking and matching. However, this problem is very challenging due to large distortion, low fingerprint quality and lack of distinctive features. The performance of existing dense registration approaches, such as image correlation and phase demodulation, are limited by manually designed features and similarity measures. To overcome the limitations of these approaches, we propose a dense fingerprint registration algorithm through convolutional neural network. The key component is a displacement regression network (DRN) that can regress pixelwise displacement field directly from coarsely aligned fingerprint images. Training groundtruth data is automatically generated by an existing dense registration algorithm without tedious manual labelling. We also propose a multiscale matching score fusion method to show the application of the proposed registration algorithm in improving fingerprint matching accuracy. Experimental results on FVC2004 DB1_A and DB2_A, and Tsinghua Distorted Fingerprint (TDF) database show that our method reaches stateoftheart registration performances.

Directed Adversarial Attacks on Fingerprints using Attributions Fingerprint recognition systems verify the identity of individuals and provide access to secure information in various commercial applications. However, with advancements in artificial intelligence, fingerprintbased security methods are vulnerable to attack. Such a breach has the potential to compromise confidential, private and valuable information. In this paper, we attack a stateoftheart fingerprint recognition system based on transfer learning. Our approach uses attribution analysis to identify the fingerprint region crucial to correct classification, and then perturbs the fingerprint using error masks derived from a neural network to generate an adversarial fingerprint.Image quality assessment metrics applied to calculate the difference between the original and perturbed fingerprints include average difference, maximum difference, normalized absolute error, and peak signal to noise ratio. On the ATVS fingerprint dataset, the differences between these values in the original and corresponding perturbed fingerprint images are negligible. Further, the VeriFinger SDK is used to detect the minutiae and perform matching between the original and perturbed fingerprints. The matching score is above 250, which reinforces the fact that there is virtually no loss between the original and perturbed fingerprints.