Fingerprint Verification Using Spectral Minutiae Representations

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points

A Fast Minutiae-Based Fingerprint Recognition System

The spectral minutiae representation is a method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require as an input a fixed-length feature vector. Based on the spectral minutiae features, this paper introduces two feature reduction algorithms: the Column Principal Component Analysis and the Line Discrete Fourier Transform feature reductions, which can efficiently compress the template size with a reduction rate of 94%. With reduced features, we can also achieve a fast minutiae-based matching algorithm. This paper presents the performance of the spectral minutiae fingerprint recognition system and shows a matching speed with 125 000 comparisons per second on a PC with Intel Pentium D processor 2.80 GHz and 1 GB of RAM. This fast operation renders our system suitable as a preselector for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching, especially in systems operating at geographical level (e.g., police patrolling) or in complex critical environments (e.g., airports)

Pseudo Identities Based on Fingerprint Characteristics

This paper presents the integrated project TURBINE which is funded under the EU 7th research framework programme. This research is a multi-disciplinary effort on privacy enhancing technology, combining innovative developments in cryptography and fingerprint recognition. The objective of this project is to provide a breakthrough in electronic authentication for various applications in the physical world and on the Internet. On the one hand it will provide secure identity verification thanks to fingerprint recognition. On the other hand it will reliably protect the biometric data through advanced cryptography technology. In concrete terms, it will provide the assurance that (i) the data used for the authentication, generated from the fingerprint, cannot be used to restore the original fingerprint sample, (ii) the individual will be able to create different "pseudo-identities" for different applications with the same fingerprint, whilst ensuring that these different identities (and hence the related personal data) cannot be linked to each other, and (iii) the individual is enabled to revoke an biometric identifier (pseudo-identity) for a given application in case it should not be used anymore

Drivers and barriers in the consistency approach for vaccine batch release testing: Report of an international workshop

Safety and potency assessment for batch release testing of established vaccines still relies partly on animal tests. An important avenue to move to batch release without animal testing is the consistency approach. This approach is based on thorough characterization of the vaccine, and the principle that the quality of subsequent batches is the consequence of the application of consistent production of batches monitored by a GMP quality system. Efforts to implement the consistency approach are supported by several drivers from industry, government, and research, but there are also several barriers that must be overcome. A workshop entitled “Consistency Approach, Drivers and Barriers” was organized, which aimed to discuss and identify drivers and barriers for the implementation of the 3Rs in the consistency approach from three different perspectives/domains (industry, regulatory and science frameworks). The workshop contributed to a better understanding of these drivers and barriers and resulted in recommendations to improve the overall regulatory processes for the consistency approach. With this report, we summarise the outcome of this workshop and intend to offer a constructive contribution to the international discussion on regulatory acceptance of the consistency approach

Face Biometrics with Renewable Templates

In recent literature, privacy protection technologies for biometric templates were proposed. Among these is the so-called helper-data system (HDS) based on reliable component selection. In this paper we integrate this approach with face biometrics such that we achieve a system in which the templates are privacy protected, and multiple templates can be derived from the same facial image for the purpose of template renewability. Extracting binary feature vectors forms an essential step in this process. Using the FERET and Caltech databases, we show that this quantization step does not significantly degrade the classification performance compared to, for example, traditional correlation-based classifiers. The binary feature vectors are integrated in the HDS leading to a privacy protected facial recognition algorithm with acceptable FAR and FRR, provided that the intra-class variation is sufficiently small. This suggests that a controlled enrollment procedure with a sufficient number of enrollment measurements is required. 2

Performance of the SCORE and Globorisk cardiovascular risk prediction models: a prospective cohort study in Dutch general practice.

BACKGROUND: GPs frequently use 10-year-risk estimations of cardiovascular disease (CVD) to identify high- risk patients. AIM: To assess the performance of four models for predicting the 10-year risk of CVD in Dutch general practice. DESIGN AND SETTING: Prospective cohort study. Routine data (2009- 2019) was used from 46 Dutch general practices linked to cause of death statistics. METHOD: The outcome measures were fatal CVD for SCORE and first diagnosis of fatal or non- fatal CVD for SCORE fatal and non-fatal (SCORE- FNF), Globorisk-laboratory, and Globorisk-office. Model performance was assessed by examining discrimination and calibration. RESULTS: The final number of patients for risk prediction was 1981 for SCORE and SCORE-FNF, 3588 for Globorisk-laboratory, and 4399 for Globorisk- office. The observed percentage of events was 18.6% (n = 353) for SCORE- FNF, 6.9% (n = 230) for Globorisk-laboratory, 7.9% (n = 323) for Globorisk-office, and 0.3% (n = 5) for SCORE. The models showed poor discrimination and calibration. The performance of SCORE could not be examined because of the limited number of fatal CVD events. SCORE-FNF, the model that is currently used for risk prediction of fatal plus non-fatal CVD in Dutch general practice, was found to underestimate the risk in all deciles of predicted risks. CONCLUSION: Wide eligibility criteria and a broad outcome measure contribute to the model applicability in daily practice. The restriction to fatal CVD outcomes of SCORE renders it less usable in routine Dutch general practice. The models seriously underestimate the 10-year risk of fatal plus non-fatal CVD in Dutch general practice. The poor model performance is possibly because of differences between patients that are eligible for risk prediction and the population that was used for model development. In addition, selection of higher-risk patients for CVD risk assessment by GPs may also contribute to the poor model performance