Fingerprint verification by fusion of optical and capacitive sensors

A few works have been presented so far on information fusion for fingerprint verification. None, however, have explicitly investigated the use of multi-sensor fusion, in other words, the integration of the information provided by multiple devices to capture fingerprint images. In this paper, a multi-sensor fingerprint verification system based on the fusion of optical and capacitive sensors is presented. Reported results show that such a multi-sensor system can perform better than traditional fingerprint matchers based on a single sensor. (C) 2004 Elsevier B.V. All rights reserved

LivDet 2017 Fingerprint Liveness Detection Competition 2017

Fingerprint Presentation Attack Detection (FPAD) deals with distinguishing images coming from artificial replicas of the fingerprint characteristic, made up of materials like silicone, gelatine or latex, and images coming from alive fingerprints. Images are captured by modern scanners, typically relying on solid-state or optical technologies. Since from 2009, the Fingerprint Liveness Detection Competition (LivDet) aims to assess the performance of the state-of-the-art algorithms according to a rigorous experimental protocol and, at the same time, a simple overview of the basic achievements. The competition is open to all academics research centers and all companies that work in this field. The positive, increasing trend of the participants number, which supports the success of this initiative, is confirmed even this year: 17 algorithms were submitted to the competition, with a larger involvement of companies and academies. This means that the topic is relevant for both sides, and points out that a lot of work must be done in terms of fundamental and applied research.Comment: presented at ICB 201

Statistical meta-analysis of presentation attacks for secure multibiometric systems

Prior work has shown that multibiometric systems are vulnerable to presentation attacks, assuming that their matching score distribution is identical to that of genuine users, without fabricating any fake trait. We have recently shown that this assumption is not representative of current fingerprint and face presentation attacks, leading one to overestimate the vulnerability of multibiometric systems, and to design less effective fusion rules. In this paper, we overcome these limitations by proposing a statistical meta-model of face and fingerprint presentation attacks that characterizes a wider family of fake score distributions, including distributions of known and, potentially, unknown attacks. This allows us to perform a thorough security evaluation of multibiometric systems against presentation attacks, quantifying how their vulnerability may vary also under attacks that are different from those considered during design, through an uncertainty analysis. We empirically show that our approach can reliably predict the performance of multibiometric systems even under never-before-seen face and fingerprint presentation attacks, and that the secure fusion rules designed using our approach can exhibit an improved trade-off between the performance in the absence and in the presence of attack. We finally argue that our method can be extended to other biometrics besides faces and fingerprints

LivDet in Action - Fingerprint Liveness Detection Competition 2019

The International Fingerprint liveness Detection Competition (LivDet) is an open and well-acknowledged meeting point of academies and private companies that deal with the problem of distinguishing images coming from reproductions of fingerprints made of artificial materials and images relative to real fingerprints. In this edition of LivDet we invited the competitors to propose integrated algorithms with matching systems. The goal was to investigate at which extent this integration impact on the whole performance. Twelve algorithms were submitted to the competition, eight of which worked on integrated systems.Comment: Preprint version of a paper accepted at ICB 201

Preliminary Results on a "Real" Iris Recognition System under Challenging Operational Conditions

Iris recognition algorithms have recently demonstrated excellent performance in the authentication task. In this paper, we present a technology transfer project for the development and testing of a biometric recognition system under challenging operational conditions. Due to the stringent operational requirements, the design and implementation of the system included a phase of selecting technologically advanced hardware. The lack of corresponding data sets implied a novel acquisition step. The evaluation phase is preliminary as the data set is being expanded for the acquisition of new samples capable of highlighting the system’s critical issues. Current samples were acquired in very different lighting conditions and in the presence of glasses, which was not yet done in the literature. In addition to the selected hardware, such data allowed us to simulate a realistic environmental context for the project’s final prototype

On the interoperability of capture devices in fingerprint presentation attacks detection

Abstract A presentation attack consists in submitting to the fingerprint capture device an artificial replica of the finger of the targeted client. If the sensor is not equipped with an appropriate algorithm aimed to detect the fingerprint spoof, the system processes the obtained image as a one belonging to a real fingerprint. In order to face this problem, several presentation attacks detection (PAD) algorithms have been proposed so far. Current methods heavily rely on features extracted from a large data set of fake and real fingerprint images, and an appropriate classifier trained with such data to distinguish between live (real) and fake (spoof) fingerprint images. Building such data set requires a significant effort for fabricating samples of fake fingerprints, with the most effective materials used to circumvent the sensor. Interesting and promising results have been obtained, but they also suggest that the PAD is tailored on the particular sensor. Small and significant differences also occur when a novel version of the same sensor is released, and this may affect the PAD. Therefore, making a PAD interoperable is among the main current issues when considering fingerprints as the first level of protection and security of logical or physical resources. This paper is a first attempt to assess at which extent the sensor interoperability can be an issue for fingerprint PADs and to eventually propose a solution to this limitation. In particular, textural features will be under focus and a feature space transformation method based on the least square is proposed

3D Face Reconstruction: the Road to Forensics

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them

3D Face Reconstruction: the Road to Forensics

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them.Comment: The manuscript has been accepted for publication in ACM Computing Surveys. arXiv admin note: text overlap with arXiv:2303.1116