Biometric Matchers Research Articles

Overview of the combination of biometric matchers

Biometric identity verification refers to technologies used to measure human physical or behavioral characteristics, which offer a radical alternative to passports, ID cards, driving licenses or PIN numbers in authentication. Since biometric systems present several limitations in terms of accuracy, universality, distinctiveness, acceptability, methods for combining biometric matchers have attracted increasing attention of researchers with the aim of improving the ability of systems to handle poor quality and incomplete data, achieving scalability to manage huge databases of users, ensuring interoperability, and protecting user privacy against attacks. The combination of biometric systems, also known as “biometric fusion”, can be classified into unimodal biometric if it is based on a single biometric trait and multimodal biometric if it uses several biometric traits for person authentication.The main goal of this study is to analyze different techniques of information fusion applied in the biometric field. This paper overviews several systems and architectures related to the combination of biometric systems, both unimodal and multimodal, classifying them according to a given taxonomy. Moreover, we deal with the problem of biometric system evaluation, discussing both performance indicators and existing benchmarks.As a case study about the combination of biometric matchers, we present an experimental comparison of many different approaches of fusion of matchers at score level, carried out on three very different benchmark databases of scores. Our experiments show that the most valuable performance is obtained by mixed approaches, based on the fusion of scores. The source code of all the method implemented for this research is freely available for future comparisons11www.dei.unipd.it/node/2357.After a detailed analysis of pros and cons of several existing approaches for the combination of biometric matchers and after an experimental evaluation of some of them, we draw our conclusion and suggest some future directions of research, hoping that this work could be a useful start point for newer research.

Review Paper on Applications of Principal Component Analysis in Multimodal Biometrics System

Unimodal biometric systems are susceptible to a variety of problems such as noisy data, intra-class variations, limited degrees of freedom, non-universality, spoof attacks and unacceptable error rates. Some of these limitations can be addressed by deploy multimodal biometric systems that integrates the evidence presented by multiple sources of information The proposed system provides effective fusion scheme that combines information presented by the multiple domain experts based on the Rank level fusion integration method, thereby increasing the efficiency of the system which is not possible by the unimodal biometric system. The proposed multimodal biometric system has a number of unique qualities, including principal component analysis and fisher's linear discriminate methods for individual matchers authentication. The novel rank level fusion method is used in order to consolidate the results obtained from different biometric matchers.

An Effective Security Based Wireless Information System Based on Fusion Principal Component Analysis

In the real world applications, wireless networks are an integral part of day-to-day life for many people, with businesses and home users relying on them for connectivity and communication. This paper examines the problems relating to the topic of wireless security and the background literature. The biometric systems often face limitations because of sensitivity to noise, intra class invariability, data quality, and other factors. Improving the performance of individual matchers in the aforementioned situation may not be effective. Multi biometric systems are used to overcome this problem by providing multiple pieces of evidence of the same identity. This system provides effective fusion scheme that combines information presented by the multiple domain experts based on the Rank level fusion integration method, thereby increasing the efficiency of the system which is not possible by the unimodal biometric system. The proposed multimodal biometric system has a number of unique qualities, starting from utilizing principal component analysis and fisher's linear discriminant methods for individual matchers authentication and the novel rank level fusion method is used in order to consolidate the results obtained from different biometric matchers. The ranks of the individual matchers are combined using highest rank, Borda count, and logistic regression method. From the results it can be concluded that the overall performances of the wireless security based multi biometric systems are improved even in the presence of quality of data.

Combining biometric matchers by means of machine learning and statistical approaches

In this paper we study both machine learning and statistical approaches for combining fingerprint matchers of the FVC2006 competition. We investigate not only which is the best fusion approach, but also the correlation among the state-of-the-art matchers for fingerprint verification and scanner interoperability of the fusion techniques. Several tests are performed on all the four FVC2006 datasets, using a leave-one-out dataset testing protocol, i.e., the training phase is conducted on the datasets not used in the testing phase, so it is possible to study the pros and cons of machine learning and statistical approaches when different scanners are used in the training and testing phases.This work confirms that the fusion of different state-of-the-art fingerprint matchers can lead to a significant performance gain with respect to a single matcher.

Bi Modal Person Identification System

Face recognition is one of the most important applications of computer vision in recent years. The developed multimodal biometric system possesses a number of unique qualities, starting from utilizing principal component analysis and Fisher's linear Discriminant methods for individual matcher's identity authentication and utilizes the novel feature fusion method to consolidate the results obtained from different biometric matchers. In this paper, we present a bimodal face-finger recognition system that fuses results from both Principal Component Analysis and Fisher face projections. The proposed approach is tested on a real database consisting of 500 images and shows promising results compared to other techniques. The main goal of bi modal identification system is to develop the security system for the areas that require high level of security. The Receiver Operating Characteristics also shows that the proposed method is superior compared to other techniques under study.

Likelihood ratio based features for a trained biometric score fusion

In this work, we present a novel trained method for combining biometric matchers at the score level. The new method is based on a combination of machine learning classifiers trained using the match scores from different biometric approaches as features. The parameters of a finite Gaussian mixture model are used for modelling the genuine and impostor score densities during the fusion step.Several tests on different biometric verification systems (related to fingerprints, palms, fingers, hand geometry and faces) show that the new method outperforms other trained and non-trained approaches for combining biometric matchers.We have tested some different classifiers, support vector machines, AdaBoost of neural networks, and their random subspace versions, demonstrating that the choice for the proposed method is the Random Subspace of AdaBoost.

ON THE DIFFERENCE BETWEEN OPTIMAL COMBINATION FUNCTIONS FOR VERIFICATION AND IDENTIFICATION SYSTEMS

We have investigated different scenarios of combining pattern matchers. The combination problem can be viewed as a construction of a postprocessing classifier operating on the matching scores of the combined matchers. The optimal combination algorithm for verification systems corresponds to the likelihood ratio combination function. It can be implemented by the direct reconstruction of this function with genuine and impostor score density approximations. However, the optimal combination algorithm for identification systems is difficult to express analytically. We will show that this difficulty is caused by the dependencies between matching scores assigned to different classes by the same classifier. The experiments on the large sets of scores from handwritten word recognizers operating on postal images and biometric matchers (NIST biometric score set BSSR1) confirm the existence of such dependencies and that the optimal combination functions for verification and identification systems are different.

Multimodal Biometric System Using Rank-Level Fusion Approach

In many real-world applications, unimodal biometric systems often face significant limitations due to sensitivity to noise, intraclass variability, data quality, nonuniversality, and other factors. Attempting to improve the performance of individual matchers in such situations may not prove to be highly effective. Multibiometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. These systems help achieve an increase in performance that may not be possible using a single-biometric indicator. This paper presents an effective fusion scheme that combines information presented by multiple domain experts based on the rank-level fusion integration method. The developed multimodal biometric system possesses a number of unique qualities, starting from utilizing principal component analysis and Fisher's linear discriminant methods for individual matchers (face, ear, and signature) identity authentication and utilizing the novel rank-level fusion method in order to consolidate the results obtained from different biometric matchers. The ranks of individual matchers are combined using the highest rank, Borda count, and logistic regression approaches. The results indicate that fusion of individual modalities can improve the overall performance of the biometric system, even in the presence of low quality data. Insights on multibiometric design using rank-level fusion and its performance on a variety of biometric databases are discussed in the concluding section.

A supervised method to discriminate between impostors and genuine in biometry

In this paper, we describe a supervised technique that allows to develop a more robust biometric system with respect to those based directly on the similarities of the biometric matchers or on the similarities normalised by the unconstrained cohort normalisation. In order to discriminate between genuine and impostors a quadratic discriminant classifier is trained using four features: the similarities of the biometric matcher; the similarities of the biometric matcher after the unconstrained cohort normalisation (UCN); the average scores among the test pattern and the users that belong to the background model; the difference between the user-specific threshold and the user-independent threshold. The proposed technique is validated by extensive experiments carried out on several biometric datasets (palm, finger, 2D and 3D faces, and ear). The experimental results demonstrate that the capabilities provided by our supervised method can significantly improve the performance of a standard biometric matcher or the performance of the standard UCN.

Use of Identification Trial Statistics for the Combination of Biometric Matchers

Combination functions typically used in biometric identification systems consider as input parameters only those matching scores which are related to a single person in order to derive a combined score for that person. We discuss how such methods can be extended to utilize the matching scores corresponding to all people. The proposed combination methods account for dependencies between scores output by any single participating matcher. Our experiments demonstrate the advantage of using such combination methods when dealing with a large number of classes, as is the case with biometric person identification systems. The experiments are performed on the National Institute of Standards and Technology BSSR1 dataset and the combination methods considered include the likelihood ratio, neural network, and weighted sum.

Over-complete feature generation and feature selection for biometry

In this paper a novel method for obtaining an appropriate representation of patterns is presented. The information is extracted using an over-complete global feature combination, and then the most useful features are selected by sequential forward floating selection (SFFS). This new method has been tested in two problems: trained integration of iris and face biometrics; on-line signature verification system based on global information and a one-class classifier (Parzen Window Classifier). To the best of our knowledge, this is the first work that studies and proposes a set of ''artificial'' features for combining biometric matchers, created starting from the scores of the matchers. We show that a classifier trained on such set of features gains a noticeable performance improvement with respect to fixed fusion rules and other trained fusion methods. Moreover, we show that an on-line signature matcher based on the ''artificial'' features gains a noticeable performance improvement with respect to a matcher based on the ''original'' global features.

An open framework for distributed biometric authentication in a web environment

Large-scale deployment of biometric systems for web-based services has to tackle technological issues related to security, interoperability and accuracy, and social issues related to privacy protection and biometric acquisition process acceptance. The variety of biometric traits, capturing devices, targeted populations and working scenarios makes the development of a universal solution for all-purpose deployment quite a difficult task. This paper describes the design, implementation and applicability of an open framework for distributed biometric authentication oriented to access-control in web environment. The open principle makes this framework a novel and practical development tool for testing and integrating biometric algorithms and devices from third parties. Special attention has been paid to security and interoperability standards to ease concurrent integration and testing of biometric trait matchers developed by different laboratories or companies. Finally, in order to demonstrate the versatility and usability of the framework, we describe the construction process for a distributed multibiometric database acquisition tool based on this framework.