Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

From the privacy perspective most concerns arise from the storage and misuse of biometric data (Cimato et al., 2009). ... is provided with a in-depth discussion of the state-of-the-art in iris biometric cryptosystems, which completes this work

A Multimodal Technique for an Embedded Fingerprint Recognizer in Mobile Payment Systems

The development and the diffusion of distributed systems, directly connected to recent communication technologies, move people towards the era of mobile and ubiquitous systems. Distributed systems make merchant-customer relationships closer and more flexible, using reliable e-commerce technologies. These systems and environments need many distributed access points, for the creation and management of secure identities and for the secure recognition of users. Traditionally, these access points can be made possible by a software system with a main central server. This work proposes the study and implementation of a multimodal technique, based on biometric information, for identity management and personal ubiquitous authentication. The multimodal technique uses both fingerprint micro features (minutiae) and fingerprint macro features (singularity points) for robust user authentication. To strengthen the security level of electronic payment systems, an embedded hardware prototype has been also created: acting as self-contained sensors, it performs the entire authentication process on the same device, so that all critical information (e.g. biometric data, account transactions and cryptographic keys), are managed and stored inside the sensor, without any data transmission. The sensor has been prototyped using the Celoxica RC203E board, achieving fast execution time, low working frequency, and good recognition performance

Analysis of Fingerprint Image to Verify a Person

Identification and authentication technologies are increasing day by day to protect people and goods from crime and terrorism. This paper is aimed to discuss fingerprint technology in depth and analysis of fingerprint image. Verify a person with a highlight on fingerprint matching. Some fingerprint matching algorithms are analysed and compared. The outcomes of the analysis has identified some major issues or factors of fingerprinting, which are location, rotation, clipping, noise, non-linear distortion sensitiveness/ insensitiveness properties, computational cost and accuracy level of fingerprint matching algorithms. Also a new fingerprint matching algorithm proposed in this research work. The proposed algorithm has used Euclidean distance, angle difference, type as matching parameters instead of specific location parameter (like, x or y coordinates), which makes the algorithm location and rotation insensitive. The matching of local neighbourhoods at each stage makes the algorithm non-linear distortion insensitive

Fingerprint Enhancement Algorithm Based-on Gradient Magnitude for the Estimation of Orientation Fields

An accurate estimation of fingerprint orientation fields is an important step in the fingerprint classification process. Gradient-based approaches are often used for estimating orientation fields of ridge structures but this method is susceptible to noise. Enhancement of fingerprint images improves the ridge-valley structure and increases the number of correct features thereby conducing the overall performance of the classification process. In this paper, we propose an algorithm to improve ridge orientation textures using gradient magnitude. That algorithm has four steps; firstly, normalization of fingerprint image, secondly, foreground extraction, thirdly, noise areas identification and marking using gradient coherence and finally, enhancement of grey level. We have used standard fingerprint database NIST-DB14 for testing of proposed algorithm to verify the degree of efficiency of algorithm. The experiment results suggest that our enhanced algorithm achieves visibly better noise resistance with other methods

Fingerprint-based biometric recognition allied to fuzzy-neural feature classification.

The research investigates fingerprint recognition as one of the most reliable biometrics identification methods. An automatic identification process of humans-based on fingerprints requires the input fingerprint to be matched with a large number of fingerprints in a database. To reduce the search time and computational complexity, it is desirable to classify the database of fingerprints into an accurate and consistent manner so that the input fingerprint is matched only with a subset of the fingerprints in the database. In this regard, the research addressed fingerprint classification. The goal is to improve the accuracy and speed up of existing automatic fingerprint identification algorithms. The investigation is based on analysis of fingerprint characteristics and feature classification using neural network and fuzzy-neural classifiers.The methodology developed, is comprised of image processing, computation of a directional field image, singular-point detection, and feature vector encoding. The statistical distribution of feature vectors was analysed using SPSS. Three types of classifiers, namely, multi-layered perceptrons, radial basis function and fuzzy-neural methods were implemented. The developed classification systems were tested and evaluated on 4,000 fingerprint images on the NIST-4 database. For the five-class problem, classification accuracy of 96.2% for FNN, 96.07% for MLP and 84.54% for RBF was achieved, without any rejection. FNN and MLP classification results are significant in comparison with existing studies, which have been reviewed

A Biometric Approach to Prevent False Use of IDs

What is your username? What is your password? What is your PIN number? These are some of the commonly used key questions users need to answer accurately in order to verify their identity and gain access to systems and their own data. Passwords, Personal Identification Numbers (PINs) and ID cards are different means of tokens used to identify a person, but these can be forgotten, stolen or lost. Currently, University of Hertfordshire (UH) carries out identity checks by checking the photograph on an ID card during exams. Other processes such as attendance monitoring and door access control require tapping the ID card on a reader. These methods can cause issues such as unauthorised use of ID card on attendance system and door access system if ID card is found, lost or borrowed. During exams, this could lead to interruptions when carrying out manual checks. As the invigilator carries out checks whilst the student is writing an exam, it is often difficult to see the student’s face as they face down whilst writing the exam. They cannot be disturbed for the ID check process. Students are also required to sign a manual register as they walk into the exam room. This process is time consuming. A more robust approach to identification of individuals that can avoid the above mentioned limitations of the traditional means, is the use of biometrics. Fingerprint was the first biometric modality that has been used. In comparison to other biometric modalities such as signature and face recognition, fingerprint is highly unique, accepted and leads to a more accurate matching result. Considering these properties of fingerprint biometrics, it has been explored in the research study presented in this thesis to enhance the efficiency and the reliability of the University’s exam process. This thesis focuses on using fingerprint recognition technology in a novel approach to check identity for exams in a University environment. Identifying a user using fingerprints is not the only aim of this project. Convenience and user experience play vital roles in this project whilst improving speed and processes at UH

A new three-stage scheme for fingerprint enhancement and its impact on fingerprint recognition

In order to provide safety and security from fraudulent acts, it is necessary to use a reliable biometric identifier. Fingerprint is considered to be one of most effective biometric identifiers because of its universal characteristics. The recognition rate of identification/verification systems depends to a great extent on the quality of the fingerprint image. In a fingerprint recognition system, there are two main phases: 1) extraction of suitable features of fingerprints, and 2) fingerprint matching using those extracted features to find the correspondence and similarity between the fingerprint images. The low quality of fingerprint images provides false minutiae at the stage of feature extraction and reduces the recognition rate of minutiae-based fingerprint matching systems. Use of enhanced fingerprint images improves the recognition rate but at the expense of a substantially increased complexity. The objective of this research is to develop an efficient and cost-effective scheme for enhancing fingerprint images that can improve minutiae extraction rate as well as effectively improve the recognition rate of a minutiae-based fingerprint matching system. In the first part of this thesis, a novel low-complexity three-stage scheme for the enhancement of fingerprint images is developed. In the first stage of the scheme, a linear diffusion filter driven by an orientation field is designed to enhance the low-quality fingerprint image. The computational complexity is reduced by using a simple gradient-based method for estimating the orientation field and by using a small number of iterations. Although some of the broken ridges in the fingerprint image are partially connected after the first stage, this stage has a limitation of not being able to connect ridges broken with wide creases, and also not being able to recover ridges in the smeared regions. To overcome the shortcomings of the first stage, the fingerprint image obtained after the first-stage enhancement is passed through a compensation filter in the second stage. Although the broken ridges in the enhanced fingerprint image after the second stage are fully connected, the ridges affected by smears are only partially recovered. Hence, the output obtained from the second stage is passed through the third-stage enhancement, which has two phases: short-time Fourier transform (STFT) analysis and enhancement by an angular filter. In the first phase, a Gaussian spectral window is used in order to perform the STFT and this window helps to reduce the blocking effect in the enhanced image. In the second phase, the image obtained from the STFT is passed through an angular filter, which significantly improves the overall quality of the fingerprint image. In the second part of this thesis, the effectiveness and usefulness of the proposed enhancement scheme are examined in fingerprint feature extraction and matching for fingerprint recognition applications. For this purpose, a minutiae extraction algorithm is first applied to extract minutiae from fingerprint images and then a minutia-based matching algorithm is applied to the set of extracted minutiae using a hybrid shape and orientation descriptor in order to find similarity between a pair of fingerprints. Extensive experiments are conducted throughout this thesis using a number of challenging benchmark databases chosen from FVC2000, FVC2002 and FVC2004. Simulation results demonstrate not only the effectiveness of the proposed enhancement scheme in improving the subjective and objective qualities of fingerprint images, but also a superior minutiae extraction rate and a recognition accuracy of the fingerprint images enhanced by the proposed scheme at a reduced computational complexity

BioTwist - overcoming severe distortions in ridge-based biometrics for successful identication

Biometrics rely on a physical trait's permanence and stability over time, as well as its individuality, robustness and ease to be captured. Challenges arise when working with newborns or infants because of the tininess and fragility of an infant's features, their uncooperative nature and their rapid growth. The last of these is particularly relevant when one tries to verify an infant's identity based on captures of a biometric taken at an earlier age. Finding a physical trait that is feasible for infants is often referred to as the infant biometric problem. This thesis explores the quality aspect of adult fingermarks and the correlation between image quality and the mark’s usefulness for an ongoing forensic investigation, and researches various aspects of the “ballprint” as an infant biometric. The ballprint, the friction ridge skin area of the foot pad under the big toe, exhibits similar properties to fingerprint but the ball possesses larger physical structures and a greater number of features. We collected a longitudinal ballprint database from 54 infants within 3 days of birth, at two months old, at 6 months and at 2 years. It has been observed that the skin of a newborn's foot dries and cracks so the ridge lines are often not visible to the naked eye and an adult fingerprint scanner cannot capture them. This thesis presents the physiological discovery that the ballprint grows isotropically during infancy and can be well approximated by a linear function of the infant's age. Fingerprint technology developed for adult fingerprints can match ballprints if they are adjusted by a physical feature (the inter-ridge spacing) to be of a similar size to adult fingerprints. The growth in ballprint inter-ridge spacing mirrors infant growth in terms of length/height. When growth is compensated for by isotropic rescaling, impressive verification scores are achieved even for captures taken 22 months apart. The scores improve even further when low-quality prints are rejected; the removal of the bottom third improves the Equal Error Rate from 1-2% to 0%. In conclusion, this thesis demonstrates that the ballprint is a feasible solution to the infant biometric problem