The fuzzy vault scheme is a cryptographic primitive that can be used to protect human fingerprint templates where stored. Analyses for most implementations account for brute-force security only. There are, however, other risks that have to be consider, such as false-accept attacks, record multiplicity attacks, and information leakage from auxiliary data, such as alignment parameters. In fact, the existing work lacks analyses of these weaknesses and are even susceptible to a variety of them. In view of these vulnerabilities, we redesign a minutiae-based fuzzy vault implementation preventing an adversary from running attacks via record multiplicity. Furthermore, we propose a mechanism for robust absolute fingerprint prealignment. In combination, we obtain a fingerprint-based fuzzy vault that resists known record multiplicity attacks and that does not leak information about the protected fingerprints from auxiliary alignment data. By experiments, we evaluate the performance of our security-improved implementation that, even though it has slight usability merits as compared with other minutiae-based implementations, provides improved security. However, despite heavy efforts spent in improving security, our implementation is, like all other implementations based on a single finger, subjected to a fundamental security limitation related to the false acceptance rate, i.e., false-accept attack. Consequently, this paper supports the notion that a single finger is not sufficient to provide acceptable security. Instead, implementations for multiple finger or even multiple modalities should be deployed the security of which may be improved by the technical contributions of this paper.

One of the main reason for false recognition is noise added to fingerprint images during the acquisition step. Hence, the improvement of the enhancement step affects general accuracy of automatic recognition systems. In one of our previous publications we introduced hierarchically linked extended features - the new set of features which not only includes additional fingerprint features individually but also contains the information about their relationships such as line adjacency information at minutiae points or links between neighbouring fingerprint lines. In this work we present the application of the extended features to preprocessing and enhancement. We use structural information for improving the segmentation step, as well as connecting disrupted fingerprint lines and recovering missing minutiae. Experiments show a decrease in the error rate in matching.

Orientation field (OF) estimation is a crucial preprocessing step in fingerprint image processing. In this paper, we present a novel method for OF estimation that uses traced ridge and valley lines. This approach provides robustness against disturbances caused, e.g., by scars, contamination, moisture, or dryness of the finger. It considers pieces of flow information from a larger region and makes good use of fingerprint inherent properties like continuity of ridge flow perpendicular to the flow. The performance of the line-sensor method is compared with the gradients-based method and a multiscale directional operator. Its robustness is tested in experiments with simulated scar noise which is drawn on top of good quality fingerprint images from the FVC2000 and FVC2002 databases. Finally, the effectiveness of the line-sensor-based approach is demonstrated on 60 naturally poor quality fingerprint images from the FVC2004 database. All orientations marked by a human expert are made available at the journal's and the authors' website for comparative tests.