Development modeling methods of analysis and synthesis of fingerprint deformations images

The current study is to develop modeling methods, Analysis and synthesis of fingerprints deformations images and their application in problems of automatic fingerprint identification. In the introduction justified urgency of the problem, is given a brief description of thematic publications. In this study will review of modern technologies of biometric technologies and methods of biometric identification, the review of fingerprint identification systems, investigate for distorting factors. The influence of deformations is singled out, the causes of deformation of fingerprints are analyzed. The review of modern ways of the account and modeling of deformations in problems of automatic fingerprint identification is given. The scientific novelty of the work is the development of information technologies for the analysis and synthesis of deformations of fingerprint images. The practical value of the work in the application of the developed methods, algorithms and information technologies in fingerprints identification systems. In addition, it has been found that our paper "devoted to research methods and synthesis of the fingerprint deformations" is a more appropriate choice than other papers

Surface Networks

We study data-driven representations for three-dimensional triangle meshes, which are one of the prevalent objects used to represent 3D geometry. Recent works have developed models that exploit the intrinsic geometry of manifolds and graphs, namely the Graph Neural Networks (GNNs) and its spectral variants, which learn from the local metric tensor via the Laplacian operator. Despite offering excellent sample complexity and built-in invariances, intrinsic geometry alone is invariant to isometric deformations, making it unsuitable for many applications. To overcome this limitation, we propose several upgrades to GNNs to leverage extrinsic differential geometry properties of three-dimensional surfaces, increasing its modeling power. In particular, we propose to exploit the Dirac operator, whose spectrum detects principal curvature directions --- this is in stark contrast with the classical Laplace operator, which directly measures mean curvature. We coin the resulting models \emph{Surface Networks (SN)}. We prove that these models define shape representations that are stable to deformation and to discretization, and we demonstrate the efficiency and versatility of SNs on two challenging tasks: temporal prediction of mesh deformations under non-linear dynamics and generative models using a variational autoencoder framework with encoders/decoders given by SNs

Increasing the detectability of external influence on precipitation by correcting feature location in GCMs

Understanding how precipitation varies as the climate changes is essential to determining the true impact of global warming. This is a difficult task not only due to the large internal variability observed in precipitation but also because of a limited historical record and large biases in simulations of precipitation by general circulation models (GCMs). Here we make use of a technique that spatially and seasonally transforms GCM fields to reduce location biases and investigate the potential of this bias correction to study historical changes. We use two versions of this bias correction—one that conserves intensities and another that conserves integrated precipitation over transformed areas. Focussing on multimodel ensemble means, we find that both versions reduce RMS error in the historical trend by approximately 11% relative to the Global Precipitation Climatology Project (GPCP) data set. By regressing GCMs' historical simulations of precipitation onto radiative forcings, we decompose these simulations into anthropogenic and natural time series. We then perform a simple detection and attribution study to investigate the impact of reducing location biases on detectability. A multiple ordinary least squares regression of GPCP onto the anthropogenic and natural time series, with the assumptions made, finds anthropogenic detectability only when spatial corrections are applied. The result is the same regardless of which form of conservation is used and without reducing the dimensionality of the fields beyond taking zonal means. While “detectability” is dependent both on the exact methodology and the confidence required, this nevertheless demonstrates the potential benefits of correcting location biases in GCMs when studying historical precipitation, especially in cases where a signal was previously undetectable

Facilitating sensor interoperability and incorporating quality in fingerprint matching systems

This thesis addresses the issues of sensor interoperability and quality in the context of fingerprints and makes a three-fold contribution. The first contribution is a method to facilitate fingerprint sensor interoperability that involves the comparison of fingerprint images originating from multiple sensors. The proposed technique models the relationship between images acquired by two different sensors using a Thin Plate Spline (TPS) function. Such a calibration model is observed to enhance the inter-sensor matching performance on the MSU dataset containing images from optical and capacitive sensors. Experiments indicate that the proposed calibration scheme improves the inter-sensor Genuine Accept Rate (GAR) by 35% to 40% at a False Accept Rate (FAR) of 0.01%. The second contribution is a technique to incorporate the local image quality information in the fingerprint matching process. Experiments on the FVC 2002 and 2004 databases suggest the potential of this scheme to improve the matching performance of a generic fingerprint recognition system. The final contribution of this thesis is a method for classifying fingerprint images into 3 categories: good, dry and smudged. Such a categorization would assist in invoking different image processing or matching schemes based on the nature of the input fingerprint image. A classification rate of 97.45% is obtained on a subset of the FVC 2004 DB1 database

Quantifying the Limits of Fingerprint Variability

Fingerprints are one of the most widely used identification features in both the biometric and forensic fields. However, the comparison and identification of fingerprints is made difficult by fingerprint variability arising from distortion. This study quantifies the limits of fingerprint variability when subject to heavy distortion, and the variability observed in repeated inked planar impressions. Fingers were video recorded performing several distortion conditions under heavy deposition pressure: left, right, up, and down translation of the finger, clockwise and counter-clockwise torque of the finger, and planar impressions. Fingerprint templates, containing `true\u27 minutiae locations, were then created from 10 inked planar impressions for 30 separate fingers. The 30 fingers studied consisted of 10 right slant loops, 10 plain arches, and 10 plain whorls. A minimal amount of variability, .18 mm globally, was observed for minutiae in inked planar impressions. When subject to heavy distortion minutiae can be displaced by upwards of 3 mm and their orientation altered by as much as 30 degrees. Minutiae displacements of 1 mm and 10 degree changes in orientation are readily observed. The results of this study will allow fingerprint examiners to identify and understand the degree of variability that can be reasonably expected throughout the various regions of fingerprints

A Computer Vision Non-Contact 3D System to Improve Fingerprint Acquisition

The fingerprint is one of the most important biometrics, with many acquisition methods developed over the years. Traditional 2D acquisition techniques produce nonlinear distortions due to the forced flattening of the finger onto a 2D surface. These random elastic deformations often introduce matching errors, making 2D techniques less reliable. Inevitably non-contact 3D capturing techniques were developed in an effort to deal with these problems. In this study we present a novel non-contact single camera 3D fingerprint reconstruction system based on fringe projection and a new model for approximating the epidermal ridges. The 3D shape of the fingerprint is reconstructed from a single 2D shading image in two steps. First the original image is decomposed into structure and texture components by an advanced Meyer algorithm. The structural component is reconstructed by a classical fringe projection technique. The textural component, containing the fingerprint information, is restored using a specialized photometric algorithm we call Cylindrical Ridge Model (CRM). CRM is a photometric algorithm that takes advantage of the axial symmetry of the ridges in order to integrate the illumination equation. The two results are combined together to form the 3D fingerprint, which is then digitally unfolded onto a 2D plane for compatibility with traditional 2D impressions. This paper describes the prototype 3D imaging system developed along with the calibration procedure, the reconstruction algorithm and the unwrapping process of the resulting 3D fingerprint, necessary for the performance evaluation of the method.