Synthetic Fingerprint Research Articles

A review on presentation attack detection system for fake fingerprint

Fingerprint recognition systems are susceptible to artificial spoof fingerprint attacks, like molds manufactured from polymer, gelatin or Play-Doh. Presentation attack is an open issue for fingerprint recognition systems. In a presentation attack, synthetic fingerprint which is reproduced from a real user is submitted for authentication. Different sensors are used to capture the live and fake fingerprint images. A liveness detection system has been designed to defeat different classes of spoof attacks by differentiating the features of live and fake fingerprint images. In the past few years, many hardware- and software-based approaches are suggested by researchers. However, the issues still remain challenging in terms of robustness, effectiveness and efficiency. In this paper, we explore all kinds of software-based solution to differentiate between real and fake fingerprints and present a comprehensive survey of efforts in the past to address this problem.

An anisotropic interaction model for simulating fingerprints

Evidence suggests that both the interaction of so-called Merkel cells and the epidermal stress distribution play an important role in the formation of fingerprint patterns during pregnancy. To model the formation of fingerprint patterns in a biologically meaningful way these patterns have to become stationary. For the creation of synthetic fingerprints it is also very desirable that rescaling the model parameters leads to rescaled distances between the stationary fingerprint ridges. Based on these observations, as well as the model introduced by Kücken and Champod we propose a new model for the formation of fingerprint patterns during pregnancy. In this anisotropic interaction model the interaction forces not only depend on the distance vector between the cells and the model parameters, but additionally on an underlying tensor field, representing a stress field. This dependence on the tensor field leads to complex, anisotropic patterns. We study the resulting stationary patterns both analytically and numerically. In particular, we show that fingerprint patterns can be modeled as stationary solutions by choosing the underlying tensor field appropriately.

Möbius Moduli for Fingerprint Orientation Fields

We propose a novel fingerprint descriptor, namely Mobius moduli, measuring local deviation of orientation fields (OF) of fingerprints from conformal fields, and we propose a method to robustly measure them, based on tetraquadrilaterals to approximate a conformal modulus locally with one due to a Mobius transformation. Conformal fields arise by the approximation of fingerprint OFs given by zero-pole models, which are determined by the singular points and a rotation. This approximation is very coarse, e.g., for fingerprints with no singular points (arch type), the zero-pole model’s OF has parallel lines. Quadratic differential (QD) models, which are obtained from zero-pole models by adding suitable singularities outside the observation window, approximate real fingerprints much better. For example, for arch type fingerprints, parallel lines along the distal joint change slowly into circular lines around the nail furrow. Still, QD models are not fully realistic because, for example along the central axis of arch type fingerprints, ridge line curvatures usually first increase and then decrease again. It is impossible to model this with QDs, which, due to complex analyticity, also produce conformal fields only. In fact, as one of many applications of the new descriptor, we show, using histograms of curvatures and conformality indices (log of the absolute values of the Mobius moduli), that local deviation from conformality in fingerprints occurs systematically at high curvature which is not reflected by state-of-the-art fingerprint models as are used, for instance, in the well-known synthetic fingerprint generation tool SFinGe and these differences robustly discriminate real prints from SFinGe’s synthetic prints.

Automatic mapping of event landslides at basin scale in Taiwan using a Montecarlo approach and synthetic land cover fingerprints

We propose a framework to systematically generate event landslide inventory maps from satellite images in southern Taiwan, where landslides are frequent and abundant. The spectral information is used to assess the pixel land cover class membership probability through a Maximum Likelihood classifier trained with randomly generated synthetic land cover spectral fingerprints, which are obtained from an independent training images dataset. Pixels are classified as landslides when the calculated landslide class membership probability, weighted by a susceptibility model, is higher than membership probabilities of other classes. We generated synthetic fingerprints from two FORMOSAT-2 images acquired in 2009 and tested the procedure on two other images, one in 2005 and the other in 2009. We also obtained two landslide maps through manual interpretation. The agreement between the two sets of inventories is given by the Cohen’s k coefficients of 0.62 and 0.64, respectively. This procedure can now classify a new FORMOSAT-2 image automatically facilitating the production of landslide inventory maps.

Simulation of synthetic fingerprint generation using Petri nets

This study describes the generation process of a synthetic fingerprint using the Petri nets. The presented Petri net simulates this process, in general. Then, there are presented various initial markings which simulate a particular real-life scenario. The proposed Petri net could be used to make the creation process more understandable and also it gives some space for optimisation and parallelisation.

Moisture-insensitive optical fingerprint scanner based on polarization resolved in-finger scattered light.

A moisture-insensitive optical fingerprint scanner (FPS) that is based on polarization resolved in-finger light is proposed and realized. Incident visible light, which is selectively fed to a fingerprint sample via a polarization beam splitter (PBS), is deemed to be partially scattered backward by tissues associated with the skin of the finger. The backscattered light is mostly index-guided in the ridge comprising the fingerprint, which has a higher refractive index, and is drastically dispersed in the valley, which is typically filled with water or air and so has a lower index. However, when light reflects directly off the surface of the finger skin, it fundamentally prevents the scanned image from being determined. The proposed FPS produces bright and dark intensity patterns that are alternately created on the surface of the PBS and correspond to the ridges and valleys, respectively. Thus, this method can especially distinguish between a fake synthetic fingerprint and a genuine fingerprint due to its use of in-finger scattered light. The scanner has been rigorously designed by carrying out ray-optic simulations depending on the wavelength, with tissue-induced scattering taken into account. The device was constructed by incorporating a wire-grid type PBS in conjunction with visible LED sources, including blue, green and red. The scanner adopting a blue LED, which exhibits the strongest light scattering, resulted in the best fingerprint image, enabling enhanced fidelity under the wet and dry situations. Finally, a fake synthetic fingerprint could be successfully discriminated.

3D fingerprint modelling and synthesis

Two-dimensional (2D) synthetic fingerprint images have been successfully used for evaluating large-scale automated fingerprint identification systems (AFISs). However, they are limited in assessing the whole process involved in an AFIS, particularly fingerprint image acquisition and fingerprint deformation. Hence, it is desired to develop synthetic 3D fingerprints and 3D fingerprint phantoms. In this reported work, a first attempt to establish a statistical shape model of 3D fingerprints is made by first re-sampling and aligning a set of training 3D fingerprint data and then applying the principal component analysis method. On the basis of the proposed model, synthetic 3D fingerprint shapes can be generated randomly. By further mapping fingerprint textures onto the shapes, synthetic 3D fingerprints can be obtained. Example results are provided demonstrating the effectiveness of the proposed model and method. The model and code will be publicly available for academic purposes.

An Improved AM–FM-Based Approach for Reconstructing Fingerprints from Minutiae

Reconstructing fingerprint images from a given set of minutiae is an important issue in analyzing the masquerade attack of automated fingerprint recognition systems (AFRSs) and in generating large scale databases of synthetic fingerprint images for the performance evaluation of AFRSs. Existing fingerprint reconstruction methods either cannot generate visually plausible or realistic fingerprint images, or suffer from the occurrence of false minutiae in the reconstructed fingerprint images. In this paper, we analyze the underlying reason of false minutiae generated by state-of-the-art amplitude modulation–frequency modulation (AM–FM)-based methods. Furthermore, we propose an improved approach by devising a better way to cope with the branch cuts (or discontinuities) in the fingerprint ridge orientation fields, and by introducing an effective scheme to remove false minutiae from the reconstructed fingerprint images. Compared with previous AM–FM based methods, the proposed method gets rid of block effects and successfully reduces the number of false minutiae. Theoretic proofs are provided with respect to the effectiveness of the proposed method for fingerprints with multiple singular points. The proposed method has also been evaluated on public fingerprint databases. The results demonstrate that it is superior to the existing methods in reconstructing realistic fingerprint images with fewer false minutiae.

Rapid detection and isotopic measurement of discrete inorganic samples using acoustically actuated droplet ejection and extractive electrospray ionization mass spectrometry

The rapid detection, screening, and isotopic signature analysis of inorganics provide invaluable information for a variety of applications including explosive device detection, nuclear forensics, and environmental monitoring. The coupling of ultrasonic nebulization and extractive electrospray ionization (EESI) enabled the mass spectrometric (MS) detection and analysis of inorganics from microliter sample solution aliquots. Ultrasonic nebulization and acoustic pressure wave focusing within an array of exponential horn structures were utilized for the efficient atomization of discrete liquid samples ranging in volume from 3 μL to 10 μL pipetted aliquots. In conjunction with an electro-flow focusing source for extractive electrospray ionization (EESI), in-source collision-induced dissociation (CID) was utilized to enhance inorganic detection through fragmentation of adducts and reduction in chemical noise from organic compounds. The investigated system enhanced detection of the singly charged elemental cation species and provided accurate measurements of isotopic distributions for a number of metal ions. The extent of CID demonstrated the competition between ligand loss from hydrate clusters and charge reduction from the doubly charged to singly charged cations for the alkaline earth metal ions of strontium and barium. Inorganics were also detected from complex matrices, including synthetic fingerprint material and sediment, without detriment to device operation. The described system provides a versatile tool for the rapid detection, speciation, and isotopic identification of inorganic compounds at nanogram to sub-nanogram levels from microliter aliquots. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA.

Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints

In this study we show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. We propose a method based on second order extended minutiae histograms (MHs) which can distinguish between real and synthetic prints with very high accuracy. MHs provide a fixed-length feature vector for a fingerprint which are invariant under rotation and translation. This 'test of realness' can be applied to synthetic fingerprints produced by any method. In this work, tests are conducted on the 12 publicly available databases of FVC2000, FVC2002 and FVC2004 which are well established benchmarks for evaluating the performance of fingerprint recognition algorithms; 3 of these 12 databases consist of artificial fingerprints generated by the SFinGe software. Additionally, we evaluate the discriminative performance on a database of synthetic fingerprints generated by the software of Bicz versus real fingerprint images. We conclude with suggestions for the improvement of synthetic fingerprint generation.

SVM-based synthetic fingerprint discrimination algorithm and quantitative optimization strategy.

Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs). In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors—the ridge distance features, global gray features, frequency feature and Harris Corner feature—are extracted. Then, a support vector machine (SVM) is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF) kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%.

Minutiae filtering to improve both efficacy and efficiency of fingerprint matching algorithms

Fingerprint minutiae extraction is a critical issue in fingerprint recognition. Both missing and spurious minutiae hinder the posterior matching process. Spurious minutiae are more frequent than missing ones, but they can be removed by post-processing. In this work, we study the usage of a state-of-the-art minutiae extractor, MINDTCT, and we analyze its major drawback: the presence of spurious minutiae lying on the borders of the fingerprint and out its area. In order to overcome this problem, we use two different filtering approaches based on the convex hull of the minutiae and the segmentation of the fingerprint. We will analyze, supported by an exhaustive experimental study, the efficacy of these methods to remove spurious minutiae. We will evaluate both the effect on different state-of-the-art matchers and the goodness of the minutiae, by comparing the extracted minutiae with the ground-truth ones. For this purpose, the experiments have been performed on several databases of both real and synthetic fingerprints. The filters used allow us to remove spurious minutiae, resulting in more accurate results even in the case of robust matchers. The EER is improved up to 2% for good quality databases, and up to 25% for FVC databases. Additionally, the matching time is accelerated, since less minutiae are processed, attaining up to a 60% runtime reduction for the tested database.

A High Performance Fingerprint Matching System for Large Databases Based on GPU

Fingerprints are the biometric features most used for identification. They can be characterized through some particular elements called minutiae. The identification of a given fingerprint requires the matching of its minutiae against the minutiae of other fingerprints. Hence, fingerprint matching is a key process. The efficiency of current matching algorithms does not allow their use in large fingerprint databases; to apply them, a breakthrough in running performance is necessary. Nowadays, the minutia cylinder-code (MCC) is the best performing algorithm in terms of accuracy. However, a weak point of this algorithm is its computational requirements. In this paper, we present a GPU fingerprint matching system based on MCC. The many-core computing framework provided by CUDA on NVIDIA Tesla and GeForce hardware platforms offers an opportunity to enhance fingerprint matching. Through a thorough and careful data structure, computation and memory transfer design, we have developed a system that keeps its accuracy and reaches a speed-up up to 100.8× compared with a reference sequential CPU implementation. A rigorous empirical study over captured and synthetic fingerprint databases shows the efficiency of our proposal. These results open up a whole new field of possibilities for reliable real time fingerprint identification in large databases.

Using PRNG Generated Templates to Protect Fingerprint Database

AbstractFingerprint databases are widely used in background/criminal investigation and physical access control. However, the security mechanisms protecting these databases are often inadequate. Once a hacker or an insider obtains a copy of a database the fingerprints in the database can be utilized easily to conduct identity theft or impersonation. Therefore, it is extremely important to protect the database against possible misuses. In this paper we propose using synthetic fingerprint minutiae templates generated with pseudorandom number generator to protect genuine fingerprint templates. Our experimental results show that fingerprint minutiae templates can still match successfully even after being inserted with large number of foreign minutiae and that the pseudorandomly generated synthetic minutiae templates can be utilized to protect real templates.

Fingerprint-Quality Index Using Gradient Components

Fingerprint image-quality checking is one of the most important issues in fingerprint recognition because recognition is largely affected by the quality of fingerprint images. In the past, many related fingerprint-quality checking methods have typically considered the condition of input images. However, when using the preprocessing algorithm, ridge orientation may sometimes be extracted incorrectly. Unwanted false minutiae can be generated or some true minutiae may be ignored, which can also affect recognition performance directly. Therefore, in this paper, we propose a novel quality-checking algorithm which considers the condition of the input fingerprints and orientation estimation errors. In the experiments, the 2-D gradients of the fingerprint images were first separated into two sets of 1-D gradients. Then, the shapes of the probability density functions of these gradients were measured in order to determine fingerprint quality. We used the FVC2002 database and synthetic fingerprint images to evaluate the proposed method in three ways: 1) estimation ability of quality; 2) separability between good and bad regions; and 3) verification performance. Experimental results showed that the proposed method yielded a reasonable quality index in terms of the degree of quality degradation. Also, the proposed method proved superior to existing methods in terms of separability and verification performance.

A Synthetic Fingerprint Generation Method and Its Implementation

A Synthetic Fingerprint Generation Method and Its Implementation

Monitoring Substratum Hygiene Using an Everyday Complex Organic Soil: The Human Fingerprint

Human fingerprint residue is an example of a specific and complex organic-material/microorganism soil which is often present in a variety of environments that are required to be hygienic. When applied onto surfaces a fingerprint may affect cleanability and influence bacterial retention, alter topography and in some cases, may even compromise the aesthetic qualities of the material. One of the most common modes of cross contamination in hygienic environments is via the human hand but this soil/microorganism matrix is rarely studied in controlled conditions. Modifications were made to published sweat and sebum standard formulations to allow a gram positive organism (Staphylococcus aureus) and a gram negative representative (Escherichia coli) to survive in a synthetic fingerprint soil for subsequent hygienic assessment of a range of surfaces. Alongside this, a method has been developed for depositing a range of organic soils in specific quantities and arrangements (e.g., the synthetic fingerprint soil in a fingerprint pattern) onto a range of substrata to allow surfaces to be more accurately assessed for cleanability.

A physical map of the human genome. The International Human Genome Mapping Consortium. Nature 2001;409:934–941.

The consortium constructed a bacterial artificial chromosome (BAC) map and described its integration with previous landmark maps of the human genome. Clones corresponding to specific STS markers were screened with genomic BAC and PAC libraries by PCR or by probe hybridization using overgo probes. Fingerprints were generated from marker-positive clones using HindIII restriction enzyme digests. Probes were used to generate the HindIII fingerprints using the Prime-it Fluor labeling kit, and hybridized to chromosome spreads on slides with competitor DNA present. The sequenced clones were integrated using synthetic fingerprints from in silico digests. Based on the map of the whole genome to enable the selection of clones for sequencing and for the accurate assembly of the genome sequence, the consortium constructed the whole-genome bacterial artificial chromosome map and integrated it with previous landmark maps. Information from mapping efforts focused on specific regions. The consortium also obtained the integration of sequence data with the map. The clone-based map has been vital for the accurate assembly of the human genome sequence. The whole-genome bacterial artificial chromosome clones comprising the clone-based map also provides an integrated resource for analysis of chromosome structure, comparative genome hybridization, and functional genetics, including gene inactivation. Together, the human genome clone map and the anchored sequence map provide synergistic resources for future analysis of the human genome.—Hans E. Grossniklaus

Investigation of Synthetic Fingerprint Solutions★

Abstract The object of this work was to investigate the validity of use of the synthetic fingerprint solution specified in present military establishment specifications. This was accomplished by comparisons with natural fingerprints and other synthetic solutions. Two or three subjects with the most corrosive fingerprints were selected from the laboratory staff and their fingerprints were used for further comparative tests. The specification synthetic print was found more difficult to remove than the most active natural print encountered during the tests conducted. The specification synthetic fingerprint solution and other synthetic solutions investigated, despite moderate differences of concentration and constituency, were equally effective in evaluating fingerprint removing compounds. On the basis of the limited evidence available, the specification synthetic fingerprint solution corresponds more closely to the concentration and pH of hand secretions normally encountered than do other synthetic solutions, which, in the main, are based on analyses of general body sweat.