Fingerprint Classification Research Articles

Scale Invariant Feature Transform Based Fingerprint Corepoint Detection

The detection of singular points (core and delta) accurately and reliably is very important for classification and matching of fingerprints. This paper presents a new approach for core point detection based on scale invariant feature transform (SIFT). Firstly, SIFT points are extracted ,then reliability and ridge frequency criteria are applied to reduce the candidate points required to make a decision on the core point. Finally a suitable mask is applied to detect an accurate core point. Experiments on FVC2002 and FVC2004 databases show that our approach locates a unique reference point with high accuracy. Results of our approach are compared with those of the existing methods in terms of accuracy of core point detection. Defence Science Journal, 2013, 63(4), pp.402 -407 , DOI:http://dx.doi.org/10.14429/dsj.63.2708

Fingerprint classification by a hierarchical classifier

Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection.

Quantum fingerprints that keep secrets

We introduce a new type of cryptographic primitive that we call a \e{hiding fingerprinting scheme}. A (quantum) fingerprinting scheme maps a binary string of length $n$ to $d$ (qu)bits, typically $d\ll n$, such that given any string $y$ and a fingerprint of $x$, one can decide with high accuracy whether $x=y$. It can be seen that a classical fingerprint of $x$ that guarantees error $\le\eps$ necessarily reveals \asOm{\Min{n,\log(1/\eps)}} bits of information about $x$. We call a scheme \e{hiding} if it reveals \aso{\Min{n,\log(1/\eps)}} bits; accordingly, no classical scheme is hiding. }{We construct quantum hiding fingerprinting schemes. Our schemes are computationally efficient and their hiding properties are shown to be optimal.

Spatial surface coarseness analysis: technique for fingerprint spoof detection

Proposed is a technique for fingerprint spoof detection, the spatial surface coarseness analysis. This approach improves the wavelet analysis of the fingertip surface texture by introducing spatial features to the model. Thus, the accuracy of the fingerprint classification is increased to 70.09% compared with the original solution.

Survey on Fingerprint Enhancement Techniques

In the field of security, many Automatic Fingerprint Identification System (AFIS) have been developed. One of the indices for evaluating the security of such system is the quality of the fingerprint image and the efficiency of the enhancement algorithm used. The main aim of the paper is to present a survey on traditional and classical fingerprint image enhancement techniques. The performance of the enhancement algorithms are evaluated on the basis of PSNR and MSE values. Based on the above measures, the most efficient enhancement algorithm is chosen.

PyroTRF-ID: a novel bioinformatics methodology for the affiliation of terminal-restriction fragments using 16S rRNA gene pyrosequencing data

BackgroundIn molecular microbial ecology, massive sequencing is gradually replacing classical fingerprinting techniques such as terminal-restriction fragment length polymorphism (T-RFLP) combined with cloning-sequencing for the characterization of microbiomes. Here, a bioinformatics methodology for pyrosequencing-based T-RF identification (PyroTRF-ID) was developed to combine pyrosequencing and T-RFLP approaches for the description of microbial communities. The strength of this methodology relies on the identification of T-RFs by comparison of experimental and digital T-RFLP profiles obtained from the same samples. DNA extracts were subjected to amplification of the 16S rRNA gene pool, T-RFLP with the HaeIII restriction enzyme, 454 tag encoded FLX amplicon pyrosequencing, and PyroTRF-ID analysis. Digital T-RFLP profiles were generated from the denoised full pyrosequencing datasets, and the sequences contributing to each digital T-RF were classified to taxonomic bins using the Greengenes reference database. The method was tested both on bacterial communities found in chloroethene-contaminated groundwater samples and in aerobic granular sludge biofilms originating from wastewater treatment systems.ResultsPyroTRF-ID was efficient for high-throughput mapping and digital T-RFLP profiling of pyrosequencing datasets. After denoising, a dataset comprising ca. 10′000 reads of 300 to 500 bp was typically processed within ca. 20 minutes on a high-performance computing cluster, running on a Linux-related CentOS 5.5 operating system, enabling parallel processing of multiple samples. Both digital and experimental T-RFLP profiles were aligned with maximum cross-correlation coefficients of 0.71 and 0.92 for high- and low-complexity environments, respectively. On average, 63±18% of all experimental T-RFs (30 to 93 peaks per sample) were affiliated to phylotypes.ConclusionsPyroTRF-ID profits from complementary advantages of pyrosequencing and T-RFLP and is particularly adapted for optimizing laboratory and computational efforts to describe microbial communities and their dynamics in any biological system. The high resolution of the microbial community composition is provided by pyrosequencing, which can be performed on a restricted set of selected samples, whereas T-RFLP enables simultaneous fingerprinting of numerous samples at relatively low cost and is especially adapted for routine analysis and follow-up of microbial communities on the long run.

Merkel cells and the individuality of friction ridge skin

There is no definite theory yet for the mechanism by which the pattern of epidermal ridges on fingers, palms and soles forming friction ridge skin (FRS) patterns is created. For a long time growth forces in the embryonal epidermis have been believed to be involved in FRS formation. More recent evidence suggests that Merkel cells play an important part in this process as well. Here we suggest a model for the formation of FRS patterns that links Merkel cells to the epidermal stress distribution. The Merkel cells are modeled as agents in an agent based model that move anisotropically where the anisotropy is created by the epidermal stress tensor. As a result ridge patterns are created with pattern defects as they occur in real FRS patterns. As a consequence we suggest why the topology of FRS patterns is indeed unique as the arrangement of pattern defects is sensitive to the initial configuration of Merkel cells.

Speciation of Organic Matter and Heavy Metals in Urban Wastewaters from an Emerging Country

Analytical scanning and transmission electron microscopy, sequential chemical extraction, and pyrolysis–gas chromatography on solvent-extractable organic matter are used to provide both direct and indirect speciation of heavy metals and organic matter in sewage-suspended solids and in biofilms taken from an urban sewer in an emerging country. Compared to developed countries where the domestic activities represent the main source of heavy metal pollution in wastewater, the combined sewer system of an emerging country also integrates significant contributions originating from the private drinking water supply system (Zn, Cu, Cr, and Ni), industrial discharges (Cu, Mn), and road dust transported by street washing (Pb). The relative importance of those sources changes drastically over time as evidenced by the difference in metal levels recorded between weekday and weekend effluents. Nevertheless, sewer biofilms are found to provide a good averaging of contaminant loads and they can be used as environmental archives. The speciation study reveals the predominance of neoformed minerals such as sulfides and phosphates and highlights the strong biogeochemical dynamics that takes place within the sewer system. Electron microscope observations of heavy metal-bearing phases proved to be necessary to provide a consistent interpretation of chemical extraction results. The molecular characterization of the solvent-extractable organic matter from biofilms reveals the presence of classical fingerprints of domestic activity such as fecal sterols and detergents but also confirms a major contribution of petroleum by-products consistent with a traffic-related pollution.

On non-invasive 2D and 3D Chromatic White Light image sensors for age determination of latent fingerprints

The feasibility of 2D-intensity and 3D-topography images from a non-invasive Chromatic White Light (CWL) sensor for the age determination of latent fingerprints is investigated. The proposed method might provide the means to solve the so far unresolved issue of determining a fingerprints age in forensics. Conducting numerous experiments for an indoor crime scene using selected surfaces, different influences on the aging of fingerprints are investigated and the resulting aging variability is determined in terms of inter-person, intra-person, inter-finger and intra-finger variation. Main influence factors are shown to be the sweat composition, temperature, humidity, wind, UV-radiation, surface type, contamination of the finger with water-containing substances, resolution and measured area size, whereas contact time, contact pressure and smearing of the print seem to be of minor importance. Such influences lead to a certain experimental variability in inter-person and intra-person variation, which is higher than the inter-finger and intra-finger variation.Comparing the aging behavior of 17 different features using 1490 time series with a total of 41,520 fingerprint images, the great potential of the CWL technique in combination with the binary pixel feature from prior work is shown. Performing three different experiments for the classification of fingerprints into the two time classes [0, 5h] and [5, 24h], a maximum classification performance of 79.29% (kappa=0.46) is achieved for a general case, which is further improved for special cases. The statistical significance of the two best-performing features (both binary pixel versions based on 2D-intensity images) is manually shown and a feature fusion is performed, highlighting the strong dependency of the features on each other. It is concluded that such method might be combined with additional capturing devices, such as microscopes or spectroscopes, to a very promising age estimation scheme.

A large scale classification of molecular fingerprints for the chemical space representation and SAR analysis

Fingerprint-based structure representation has a broad range of applications including, but not limited to, diversity analysis, compound classification, chemical space visualization [1], activity landscape modelling and similarity searching. It has been shown that depending on the particular fingerprints used, the outcome of similarity searching [2] or activity landscapes [3] can be very different. Combining structure representations is a common practice to increase the performance of similarity searching [4]. Also, combining representations for activity landscape modelling has been proposed to generate robust descriptive SAR models [5]. However, the selection of fingerprints to be combined is not an easy task. As part of our efforts to select fingerprint representations to generate consensus representations of chemical space and activity landscapes [5,6] herein we discuss the results of a systematic comparison of more than 10 2D and 3D fingerprint representations in terms of performance in diversity analysis (as opposed to similarity searching). We employed more than 20 data sets from different sources relevant to drug discovery. In this work the widely used Tanimoto coefficient was employed. The approach presented here can be easily extended to other similarity measures, additional fingerprints and molecular databases. We also discuss the typical mean/median similarity values of selected fingerprints across databases from different sources.

A hierarchical k-means clustering based fingerprint quality classification

This paper presents a novel technique that employs a hierarchical k-means clustering for quality based classification of fingerprints for subsequent improvement in fingerprint matching results. A set of statistical and frequency features have been calculated from a fingerprint image. A hierarchical k-means clustering algorithm has been utilized to classify the fingerprint image into one of four quality classes, i.e. good, dry, normal or wet. An objective method has also been proposed to evaluate the performance of fingerprint quality classification. It has been shown through experimental results that the performance of minutiae based matcher improves when the quality of fingerprint image is incorporated in the matching stage. The false accept rate and false reject rate of minutiae based fingerprint matcher are 1.8 on FVC 2002 db1 database without utilizing fingerprint quality information. False accept rate has been reduced from 1.8 to 0.79 whereas the false reject rate is at 1.8 when fingerprint quality based threshold value is utilized. This significant improvement in the performance of the fingerprint matching system shows the effectiveness of hierarchical k-means clustering technique in quality based classification of fingerprints.

A variational formulation for fingerprint orientation modeling

Orientation pattern is an important feature for characterizing fingerprint and plays critical roles in fingerprint recognition and fingerprint classification. This paper proposes a framework for modeling the fingerprint orientation field based on the variational principle, where the orientation pattern can be estimated through solving the associated Euler-Lagrange equation. Compared with existing methods, our proposed method has the following features. Firstly, it does not require any prior information about the structure of the acquired fingerprint, such as location of singular point(s). Secondly, it explicitly provides freedom for modeling the singularity in the orientation field. Thirdly, it has less number of parameters. Comparison has been made with respect to state-of-the-arts in fingerprint orientation modeling in terms of modeling accuracy, fingerprint enhancement and singular point detection. Advantages of the proposed method are demonstrated.

Penentuan Klas Sidik Jari Berdasarkan Arah Kemiringan Ridge

Researches on fingerprint classification are generally based on its features such as core and delta. Extraction of these features are generally preceded by a variety of preprocessing. In this study the classification is done directly on the fingerprint image without preprocessing. Feature used as the basis for classification is the direction of the ridge. The direction of the ridge is determined by the slope of the blocks that are exist on every ridge. Fingerprint image is divided into blocks of size 3x3 pixels and the direction of each block is determined. Direction of the slope of the block are grouped into 8, these are north, north-east, east, south-east, south, south-west, west and north-west. The number of blocks in each direction form the basis of classification using Learning Vector Quantization network (LVQ). This study used 80 data samples from the database of FVC2004. This model obtained classification accuracy of up to 86.3%. Keywords—fingerprint, classification, ridge, LVQ

Live-scanned fingerprint classification with Markov models modified by GA

Live-scanning devices are widely used in many fields. An important difference between fingerprint images acquired by ink and paper and fingerprints acquired by live-scanning devices is completeness. Since the sensor sizes of live-scanning devices are usually smaller than an average fingerprint and users may not align their fingers properly on the sensors, only a part of a fingerprint may be scanned, resulting in the omission of some singular points. In this paper, we propose a novel approach which increases the classification performance for fingerprint images obtained by live-scanning devices. We extract ridge directional values and create Markov models. However, Markov models in each class share most transitions because fingerprints are basically circular in shape. In order to enhance the specific transitions of each class and to suppress the common transitions in the Markov models, we apply genetic algorithms. The performance of the optimized classification model using genetic algorithms was shown to be superior to the pre-optimization model. The proposed method effectively classifies live-scanned fingerprint images because this approach is based on the global feature of ridge direction, and is independent of the existence of singular points.

A Quantitative Survey of various Fingerprint Enhancement techniques

Preprocessing is an important step in the area of image processing and pattern recognition. This paper aims to present a review of recent as well as classic fingerprint image enhancement techniques. The umbrella of techniques used for evaluation varies from histogram based enhancement, frequency transformation based, Gabor filter based enhancement and its variants to composite enhancement technique. The effectiveness of enhancement techniques proposed by various researchers is evaluated on the basis of peak signal to noise ratio and equal error rate which refers to robustness and stability of identification process. Experimental results shows that incorporating the enhancement technique based on Gabor filter in wavelet domain and composite method improves equal error rate .Improved error rate and peak signal noise ratio improves the identification/verification accuracy marginally. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in enhancement of fingerprint images which is essential preprocessing step in automatic fingerprint identification and verification.

Evaluation of multiclass support vector machine classifiers using optimum threshold-based pruning technique

Support vector machine (SVM) is the state-of-the-art classifier used in real world pattern recognition applications. One of the design objectives of SVM classifiers using non-linear kernels is reducing the number of support vectors without compromising the classification accuracy. To meet this objective, decision-tree approach and pruning techniques are proposed in the literature. In this study, optimum threshold (OT)-based pruning technique is applied to different decision-tree-based SVM classifiers and their performances are compared. In order to assess the performance, SVM-based isolated digit recognition system is implemented. The performances are evaluated by conducting various experiments using speaker-dependent and multispeaker-dependent TI46 database of isolated digits. Based on this study, it is found that the application of OT technique reduces the minimum time required for recognition by a factor of 1.54 and 1.31, respectively, for speaker-dependent and multispeaker-dependent cases. The proposed approach is also applicable for other SVM-based multiclass pattern recognition systems such as target recognition, fingerprint classification, character recognition and face recognition.

Fingerprint Classification Using Least Square Orientation and Quick Propagation Algorithms

Fingerprint Classification Using Least Square Orientation and Quick Propagation Algorithms

A review on fingerprint orientation estimation

AbstractFingerprint orientation plays important roles in fingerprint enhancement, fingerprint classification, and fingerprint recognition. This paper critically reviews the primary advances on fingerprint orientation estimation. Advantages and limitations of existing methods have been addressed. Issues on future development have been discussed. Copyright © 2010 John Wiley & Sons, Ltd.

Automated fingerprint classification based on directional image tracing and core axis

A new concept on the fingerprint core axis and the method to compute it in the fingerprint images were proposed,using the continuously distributed directional image tracing.Then,a new fingerprint classification algorithm based on the directional image tracing and fingerprint core axis was developed,which classified input fingerprints into six categories:arch,tented arch,left loop,right loop,twin loop and whorl.The experimental results obtained on the Set B fingerprint database of FVC2000,FVC2002 and FVC2004,the Set A fingerprint database of FVC2004 and our fingerprint database demonstrate that this algorithm is invariant to image rotation of any degrees,and successfully separates the twin loop fingerprint from the whorl fingerprint.For the 4 000 images in our fingerprint database,a classification accuracy of 95.6% for the six-class problem has been achieved.Therefore,it has better classification performance than previously reported in the literature.

Improvement of Fingerprint Retrieval by a Statistical Classifier

The topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous works resorted to simple <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN) classification. However, the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN classifier has the drawbacks of being comparatively slow and less accurate. In this paper, we tackle this problem by first artificially expanding the set of training samples using our previously proposed spatial modeling technique. With the expanded training set, we are then able to employ a more sophisticated classifier such as the Bayes classifier for recognition. We apply the proposed method to the problem of one-to- <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> fingerprint identification and retrieval. The accuracy and speed are evaluated using the benchmarking FVC 2000, FVC 2002, and NIST-4 databases, and satisfactory retrieval performance is achieved.