Fingerprint Matching Techniques Research Articles

Minutia Extraction Based Electronic Voting Machine

The purpose of this research is to build electronic voting machine to increase transparency in election. This system uses microcontroller as the central part which registers the vote of the individual, the vote will be cost by pushing the button where for the identification of voter, figure print scanning module is used. Human finger prints are rich in minutia which are used in finger prints verification. This paper focuses on finger prints system of identification, which actually is minutia matching (MM), which is widely used in fingerprint algorithms.The technique include extraction from fingerprints the minutia points (MP). The technique is to match two fingerprint images by performing fingerprint matching technique. The main steps used for recognition are enhancement of image, segmentation of image, extraction of minutia and its matching

Latent Fingerprint Recognition using Hybrid Ant Colony Optimization and Cuckoo Search

Latent fingerprints are adapted as prominent evidence for the identification of crime suspects from ages. The unavailability of complete minutiae information, poor quality of impressions, and overlapping of multi-impressions make the latent fingerprint recognition process a challenging task. Although the contributions in the field are efficient for determining the match, there is a requirement to ameliorate the existing techniques as false identification can put the benign behind bars. This research work has amalgamated the Cuckoo Search (CS) algorithm with Ant Colony Optimization (ACO) for the recognition of latent fingerprints. It reduces the demerits of the individual cuckoo search algorithm, such as the probability of falling into local optima, the inefficient creation of nests at the boundary due to random walk and Levy flight attributes. The positive feedback mechanism of ant colony optimization makes it easy to combine with other techniques, reducing the risk of local failure and evaluating the global best solution. Prior to the evaluation of the proposed amalgamated technique on the latent fingerprint dataset of NIST SD-27, it is tested with the benchmark functions for different shapes and physical attributes. The benchmark testing and latent fingerprint evaluation result in the betterment of the amalgamated technique over the individual cuckoo search algorithm. The state-of-the-art comparison indicates that the amalgamation technique outperformed the other fingerprint matching techniques.

A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms

Amongst all biometric-based personal authentication systems, a fingerprint that gives each person a unique identity is the most commonly used parameter for personal identification. In this paper, we present an automatic fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques. Initially, a variant of adaptive histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast of fingerprint images. The fingerprint is then authenticated by picking a small amount of information from some local interest points called minutiae point features. These features are extracted from the thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresponding Harris-SURF feature vectors of two feature points is used as a feature matching similarity measure of two fingerprint images. Moreover, an iterative algorithm called RANSAC (RANdom SAmple Consensus) is applied for fine matching and to automatically eliminate false matches and incorrect match points. Quantitative experimental results achieved on FVC2002 DB1 and FVC2000 DB1 public domain fingerprint databases demonstrate the good performance and feasibility of the proposed framework in terms of achieving average recognition rates of 95% and 92.5% for FVC2002 DB1 and FVC2000 DB1 databases, respectively.

Ant colony optimisation for latent fingerprint matching

Biometric recognition is a prominent tool to recognise individuals based on their personal and biological traits like fingerprint, iris, voice, and face. Fingerprint recognition has been substantially used over the decades to identify and verify an individual's identity. In forensic investigations, fingerprint matching is one of the most reliable tools for person identification. The latent fingerprints collected from crime scenes are matched with full fingerprints for person identification. Latent fingerprints are accidentally left finger impressions with overlapping patterns, backgrounds and spoiled minutiae information. This paper aims to propose a system for automated latent fingerprint matching. The latent fingerprint images are initially enhanced to remove noise and to obtain useful information. Image enhancement includes pre-processing steps of segmentation followed by normalisation, filtering and image binarisation. Further, minutiae features are extracted from the pre-processed data and final matching is performed using an ant colony optimisation (ACO) algorithm to optimise the process of minutiae matching. The experimentation results on NIST special database 27 are computed in terms of accuracy assessment measures (precision, recall, F-score), similarity score and identification rate. The proposed system has evinced satisfactory results in comparison with other existing fingerprint matching techniques.

OPTIMIZATION OF FINGERPRINT SIZE FOR REGISTRATION

The propose algorithm finds the optimal reduced size of latent fingerprint. The algorithm accelerates the correlation methods of fingerprint registration. The Algorithm is based on decomposition and reduction of fingerprint to one dimension form by using the adoptive method of empirical modes. We choose the most appropriate internal mode to determine the minimum distance between the extremes of empirical modes. We can estimate how many times the fingerprint in the first step of the comparison can be reduced so as not to lose the accuracy of registration. This algorithm shows best results as compared to conventional fingerprint matching techniques that strongly depends on local features for registration. The algorithm was tested on latent fingerprints using FVC2002, FVC2004 and FVC2006 databases.

A Review on Biometrics Authentication System Using Fingerprint

Technology based on Biometric identification and verification is one leading research area. It deals with the concept analyzing the human body characteristics through Biometric devices for various authentications process. There are many Biometric authentication systems are available for verification process. This paper discusses the role of Fingerprint authentication. FP recognition is highly used biometric technique, because of abundance sources (i.e. ten fingers) availability for collecting data. Discussion on Fingerprint matching techniques, recognition methods and their performance analysis are made in the paper.

Ant Colony Optimization for Latent Fingerprint Matching

Biometric recognition is a prominent tool to recognise individuals based on their personal and biological traits like fingerprint, iris, voice, and face. Fingerprint recognition has been substantially used over the decades to identify and verify an individual's identity. In forensic investigations, fingerprint matching is one of the most reliable tools for person identification. The latent fingerprints collected from crime scenes are matched with full fingerprints for person identification. Latent fingerprints are accidentally left finger impressions with overlapping patterns, backgrounds and spoiled minutiae information. This paper aims to propose a system for automated latent fingerprint matching. The latent fingerprint images are initially enhanced to remove noise and to obtain useful information. Image enhancement includes pre-processing steps of segmentation followed by normalisation, filtering and image binarisation. Further, minutiae features are extracted from the pre-processed data and final matching is performed using an ant colony optimisation (ACO) algorithm to optimise the process of minutiae matching. The experimentation results on NIST special database 27 are computed in terms of accuracy assessment measures (precision, recall, F-score), similarity score and identification rate. The proposed system has evinced satisfactory results in comparison with other existing fingerprint matching techniques.

An advanced fingerprint matching using minutiae-based indirect local features

Biometric systems examine the uniqueness of an individual based on physical and behavioral characteristics. Among the known traits, fingerprint is the most significant biometric trait due to its ease of use and high accuracy. However, the efficiency of the fingerprint matching technique depends on the feature vector it uses. The ideal feature vector should be invariant to several common transformations, which usually a fingerprint capturing system is subjected to. Current work focuses to achieve such an invariance by extracting the features based on the spatial relationship among minutiae points. We propose a minutiae point based 4-dimensional local feature vector, which simultaneously satisfies six desirable feature vector properties. This feature vector definition helps us to deal with problem of missing and spurious minutiae and thus enables us to design a robust authentication system. We have substantiated the efficacy of the proposed approach with the help of a number of fingerprint instances available in FVC and NIST databases.

Performance Evaluation of Localization Accuracy for a Log-Normal Shadow Fading Wireless Sensor Network under Physical Barrier Attacks.

Localization is an apparent aspect of a wireless sensor network, which is the focus of much interesting research. One of the severe conditions that needs to be taken into consideration is localizing a mobile target through a dispersed sensor network in the presence of physical barrier attacks. These attacks confuse the localization process and cause location estimation errors. Range-based methods, like the received signal strength indication (RSSI), face the major influence of this kind of attack. This paper proposes a solution based on a combination of multi-frequency multi-power localization (C-MFMPL) and step function multi-frequency multi-power localization (SF-MFMPL), including the fingerprint matching technique and lateration, to provide a robust and accurate localization technique. In addition, this paper proposes a grid coloring algorithm to detect the signal hole map in the network, which refers to the attack-prone regions, in order to carry out corrective actions. The simulation results show the enhancement and robustness of RSS localization performance in the face of log normal shadow fading effects, besides the presence of physical barrier attacks, through detecting, filtering and eliminating the effect of these attacks.

A Robust Biometric Authentication and PIN Distribution Technique for Secure Mobile Commerce Applications

In a mobile emerging world, user authentication, service provider authentication and security is very important in mobile commerce. User authentication is performed by using fingerprint based biometric methodology. Existing system used for Mobile purchasing/payment services in handheld devices does not analyze fingerprint matching and feature extraction techniques in an efficient way. Also the existing system is not secure and accurate for m-payments applications. We propose secure, efficient and accurate m-commerce architecture for m-commerce applications. This involves fusion of Minutiae Maps (MM) and Orientation Maps (OM) for fingerprint feature extraction. The fingerprint is sent to the biometric server in a secure way using Discrete Wavelet Transform (DWT) data hiding method. User fingerprint will be checked and compared using MM and OM methods to figure out the fingerprint threshold matching score. If the threshold is 80-99% PIN distribution process is initiated, otherwise user authentication is failed. The user PIN is converted into a unique sequence and divided into two parts. Along with the user PIN, user IP address, time stamp, user ID are encrypted using RC4 (stream cipher) algorithm. Also a hash function is appended to the cipher text using Secure Hash Algorithm (SHA4). One half is verified by the authentication server and the other half is verified by the external server. After verification both the servers sends only OK message to the bank. This study looks to provide a high secure and efficient solution for m-commerce applications.

Rotational invariant fingerprint matching using local directional descriptors

Fingerprint matching techniques have been extensively investigated in the literature. Minutiae-based and image-based approaches are two major classes of fingerprint matching. Minutiae-based approaches are still not able to achieve high matching accuracy, particularly in low quality images. Now-a-days, image-based methods are given more attention as they extract more discriminatory information in comparison to minutiae-based methods. In this paper, we have proposed an image-based method, which exploits the rotation invariant local directional fixed length descriptors. This method involves preprocessing, region of interest (ROI) computation and extraction of proposed local directional fixed length descriptors. The proposed descriptors are tested on public benchmark databases FVC2002 and FVC2004 using Euclidian distance, Chi-square distance, histogram intersection, and least square support vector machine (LS-SVM). The effectiveness of the proposed method is demonstrated by comparing the experimental results with those of other image-based approaches proposed in the literature.

A Study of Biometric Approach Using Fingerprint Recognition

Fingerprint (FP) serves to identify that the person authenticating is who he/she claims to be. FP identification is popular biometric technique due to easiness in acquiring, availability of plenty sources (i.e. ten fingers) for collecting data and their established use. This paper summarizes the research work carried out in FP matching techniques, recognition methods and their performance analysis. 0.001 to 0.01% of FAR (False Acceptance Rate). The pre-requisite to match the FP is classification. The classification is treated as course level matching. The FP can be classified as whorl, right loop, arch, tented arch. In order to ensure the performance of FP identification, enhancement algorithms are needed to improve clarity of input fingerprint images.

Fingerprint image: pre- and post-processing

Automatic Fingerprint Identification Systems (AFIS) are widely used for personal identification due to uniqueness of fingerprints. Minutiae-based fingerprint matching techniques are normally used for fingerprint matching. Fingerprint matching results and their accuracy depends on presence of valid minutiae. In this paper, we present a new technique for fingerprint image post-processing. This post-processing is used to eliminate a large number of false extracted minutiae from skeletonised fingerprint images. We propose a windowing post-processing method that takes into account the neighbourhood of each minutia within defined window and check for minutia validation and invalidation. We also present a complete pre-processing system including new segmentation technique that is required to extract region of interest (ROI) accurately from a fingerprint image. The results are confirmed by visual inspections of validated minutiae of the FVC2004 reference fingerprint image database. Experimental results obtained by the proposed approach show efficient reduction of false minutiae.

A COMBINATORIAL APPROACH TO FINGERPRINT BINARIZATION AND MINUTIAE EXTRACTION USING EUCLIDEAN DISTANCE TRANSFORM

Most of the fingerprint matching techniques require extraction of minutiae that are ridge endings or bifurcations of ridge lines in a fingerprint image. Crucial to this step is either detecting ridges from the gray-level image or binarizing the image and then extracting the minutiae. In this work, we firstly exploit the property of almost equal width of ridges and valleys for binarization. Computing the width of arbitrary shapes is a nontrivial task. So, we estimate the width using Euclidean distance transform (EDT) and provide a near-linear time algorithm for binarization. Secondly, instead of using thinned binary images for minutiae extraction, we detect minutiae straightaway from the binarized fingerprint images using EDT. We also use EDT values to get rid of spurs and bridges in the fingerprint image. Unlike many other previous methods, our work depends minimally on arbitrary selection of parameters.

Fingerprint matching from minutiae texture maps

The fingerprint matching using the original FingerCode generation has proved its effectiveness but it suffers from some limitations such as the reference point localization and the recourse to the relative fingerprint pre-alignment stage. In this paper, we propose a new hybrid fingerprint matching technique based on minutiae texture maps according to their orientations. Therefore, rather than exploiting the eight fixed directions of Gabor filters for all original fingerprint images filtering process, we construct absolute images starting from the minutiae localizations and orientations to generate our weighting oriented Minutiae Codes. The extracted features are invariant to translation and rotation, which allows us avoiding the fingerprint pair relative alignment stage. Results are presented demonstrating significant improvements in fingerprint matching accuracy through public fingerprint databases.

A Fingerprint Matching Technique Based on Phase-Only Correlation

A Fingerprint Matching Technique Based on Phase-Only Correlation

An adaptive hybrid energy-based fingerprint matching technique

In this paper we present a new adaptive hybrid energy-based fingerprint matching system, which combines both minutiae information available in a fingerprint with the information of the local ridges in their vicinity. A more continuous representation of fingerprints can be obtained by using an energy-based rectangular tessellation with non-overlapped squared cells. However, a fixed tessellation is not efficient in handling non-linear deformations in fingerprints for which we propose an adaptive matching technique that uses dynamic rectangular tessellation to handle them. Each time a match is not found the dynamic tessellation increases its cell size until there is a match or cell size is greater than image size where the fingerprint is rejected. The basic idea of this system is to divide the fingerprint-matching problem into several small sub-problems that involve the use of cell energy minimization for which an iterative schema is devised. At each minimization step this schema optimizes its local energy according to the previous estimate and the observed image features. Minutiae and local ridges in their vicinity, produce different amounts of energy which form the energy vectors of the fingerprint image. In this work, we focus on the difficult problem of recognizing known fingerprints while rejecting unknown ones. Our system was tested on FVC2000 benchmark database of fingerprints and showed promising results. We show that matching performance can be improved by using energy vectors and adaptive matching, where adaptive matching reduces the effect of intra-class variations between different impressions of the same fingerprint image and energy vectors can efficiently represent fingerprints by using both information extracted from the minutiae and their local surrounding ridges.

Fingerprint matching of E. coli strains with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry of whole cells using a modified correlation approach.

We have developed a mathematical algorithm to compare and distinguish matrix-assisted laser desorption/ionization (MALDI) mass spectra of whole bacteria cells. This fingerprint matching technique eliminates the subjectivity involved in visually comparing two spectra to determine whether they match and it provides a quantitative measure of spectral similarity. Using it, we have distinguished twenty five different strains of a single bacteria species, E. coli. Cells are grown in culture, samples are prepared, and MALDI-TOF mass spectra are recorded for each strain. Pairs of spectra are compared by a modified cross-correlation procedure. This modified approach increases the sensitivity of correlation analysis to small spectral differences. The technique can be fine-tuned by varying the number of intervals into which spectra are divided.