A Novel Fingerprint Recovery Scheme using Deep Neural Network-based Learning

Abstract

Minutiae used in most fingerprint recognition devices is robust to presentation attack, but generates a high false match rate. Thus, it is applied along with orientation map or skeleton images. There has been plenty of research on security vulnerability of minutiae, whereas few research has been conducted on orientation map or skeleton images. This study analyzes vulnerability of presentation attack for skeleton images. For this purpose, it proposes a new algorithm of recovering fingerprints with the use of machine learning and skeleton image features of fingerprints. In the proposed method, we suggest the new machine learning Pix2Pix model to generate more natural images. The suggested model is developed in the way of adding a latent vector to the conventional image-to-image translation model Pix2Pix. In the experiment, fingerprints were recovered with the use of the proposed Pix2Pix model, and it was found that a fingerprint recognition device which recognized the recovered fingerprints had a high success rate of recognition. Therefore, it was proved that a fingerprint recognition device using skeleton images as well was vulnerable to presentation attack. It is expected that the algorithm proposed in this study will be very useful to many different application areas related to image processing, including biometrics, fingerprint recognition and recovery, and image surveillance.