A quality integrated spectral minutiae fingerprint recognition system

Many fingerprint recognition systems are based on minutiae matching. However, the recognition accuracy of minutiae-based matching algorithms is highly dependent on the fingerprint minutiae quality. Therefore, in this paper, we introduce a quality integrated spectral minutiae algorithm, in which the minutiae quality information is incorporated to enhance the performance of the spectral minutiae fingerprint recognition system. In our algorithm, two types of quality data are used. The first is the minutiae reliability, expressing the probability that a given point is indeed a minutia; the second is the minutiae location accuracy, quantifying the error on the minutiae location. We integrate these two types of quality information into the spectral minutiae representation algorithm and achieve a decrease of 1% in equal error rate in the experiment

Spectral representation of fingerprints

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and directions 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 a template protection scheme, which requires a fixed-length feature vector. This paper introduces the idea and algorithm of spectral minutiae representation. A correlation based spectral minutiae\ud matching algorithm is presented and evaluated. The scheme shows a promising result, with an equal error rate of 0.2% on manually extracted minutiae

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

Binary Biometrics: An Analytic Framework to Estimate the Performance Curves Under Gaussian Assumption

In recent years, the protection of biometric data has gained increased interest from the scientific community. Methods such as the fuzzy commitment scheme, helper-data system, fuzzy extractors, fuzzy vault, and cancelable biometrics have been proposed for protecting biometric data. Most of these methods use cryptographic primitives or error-correcting codes (ECCs) and use a binary representation of the real-valued biometric data. Hence, the difference between two biometric samples is given by the Hamming distance (HD) or bit errors between the binary vectors obtained from the enrollment and verification phases, respectively. If the HD is smaller (larger) than the decision threshold, then the subject is accepted (rejected) as genuine. Because of the use of ECCs, this decision threshold is limited to the maximum error-correcting capacity of the code, consequently limiting the false rejection rate (FRR) and false acceptance rate tradeoff. A method to improve the FRR consists of using multiple biometric samples in either the enrollment or verification phase. The noise is suppressed, hence reducing the number of bit errors and decreasing the HD. In practice, the number of samples is empirically chosen without fully considering its fundamental impact. In this paper, we present a Gaussian analytical framework for estimating the performance of a binary biometric system given the number of samples being used in the enrollment and the verification phase. The error-detection tradeoff curve that combines the false acceptance and false rejection rates is estimated to assess the system performance. The analytic expressions are validated using the Face Recognition Grand Challenge v2 and Fingerprint Verification Competition 2000 biometric databases

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)

Repenser le management des ressources humaines et des relations de travail

Botulinum neurotoxin A (BoNT/A) belongs to the most dangerous class of bioweapons. Despite this, BoNT/A is used to treat a wide range of common medical conditions such as migraines and a variety of ocular motility and movement disorders. BoNT/A is probably best known for its use as an antiwrinkle agent in cosmetic applications (including Botox and Dysport). BoNT/A application causes long-lasting flaccid paralysis of muscles through inhibiting the release of the neurotransmitter acetylcholine by cleaving synaptosomal-associated protein 25 (SNAP-25) within presynaptic nerve terminals. Two types of BoNT/A receptor have been identified, both of which are required for BoNT/A toxicity and are therefore likely to cooperate with each other: gangliosides and members of the synaptic vesicle glycoprotein 2 (SV2) family, which are putative transporter proteins that are predicted to have 12 transmembrane domains, associate with the receptor-binding domain of the toxin. Recently, fibroblast growth factor receptor 3 (FGFR3) has also been reported to be a potential BoNT/A receptor. In SV2 proteins, the BoNT/A-binding site has been mapped to the luminal domain, but the molecular details of the interaction between BoNT/A and SV2 are unknown. Here we determined the high-resolution crystal structure of the BoNT/A receptor-binding domain (BoNT/A-RBD) in complex with the SV2C luminal domain (SV2C-LD). SV2C-LD consists of a right-handed, quadrilateral β-helix that associates with BoNT/A-RBD mainly through backbone-to-backbone interactions at open β-strand edges, in a manner that resembles the inter-strand interactions in amyloid structures. Competition experiments identified a peptide that inhibits the formation of the complex. Our findings provide a strong platform for the development of novel antitoxin agents and for the rational design of BoNT/A variants with improved therapeutic properties

Introduction

Over the past decades a large range of security primitives has been developed to protect digital information. These primitives have solved many traditional security problems and achieved a high level of sophistication. Their security properties are well understood. An intrinsic property, vital for secure operation, is that they are extremely sensitive to small variations in their input.</p

Introduction

Over the past decades a large range of security primitives has been developed to protect digital information. These primitives have solved many traditional security problems and achieved a high level of sophistication. Their security properties are well understood. An intrinsic property, vital for secure operation, is that they are extremely sensitive to small variations in their input