Combining multiple biometrics to protect privacy

As biometrics are gaining popularity, there is increased concern over the loss of privacy and potential misuse of biometric data held in central repositories. The association of fingerprints with criminals raises further concerns. On the other hand, the alternative suggestion of keeping biometric data in smart cards does not solve the problem, since forgers can always claim that their card is broken to avoid biometric verification altogether. We propose a biometric authentication framework which uses two separate biometric features combined to obtain a non-unique identifier of the individual, in order to address privacy concerns. As a particular example, we demonstrate a fingerprint verification system that uses two separate fingerprints of the same individual. A combined biometric ID composed of two fingerprints is stored in the central database and imprints from both fingers are required in the verification process, lowering the risk of misuse and privacy loss. We show that the system is successful in verifying a person’s identity given both fingerprints, while searching the combined fingerprint database using a single fingerprint, is impractical

In-ear EEG biometrics for feasible and readily collectable real-world person authentication

The use of EEG as a biometrics modality has been investigated for about a decade, however its feasibility in real-world applications is not yet conclusively established, mainly due to the issues with collectability and reproducibility. To this end, we propose a readily deployable EEG biometrics system based on a `one-fits-all' viscoelastic generic in-ear EEG sensor (collectability), which does not require skilled assistance or cumbersome preparation. Unlike most existing studies, we consider data recorded over multiple recording days and for multiple subjects (reproducibility) while, for rigour, the training and test segments are not taken from the same recording days. A robust approach is considered based on the resting state with eyes closed paradigm, the use of both parametric (autoregressive model) and non-parametric (spectral) features, and supported by simple and fast cosine distance, linear discriminant analysis and support vector machine classifiers. Both the verification and identification forensics scenarios are considered and the achieved results are on par with the studies based on impractical on-scalp recordings. Comprehensive analysis over a number of subjects, setups, and analysis features demonstrates the feasibility of the proposed ear-EEG biometrics, and its potential in resolving the critical collectability, robustness, and reproducibility issues associated with current EEG biometrics

Machine Learning for Biometrics

Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identifies relevant research issues. We focus on three areas of interest: offline methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and influential machine learning approaches in the context of specific biometrics applications, we hope to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems

Optimal decision fusion and its application on 3D face recognition

Fusion is a popular practice to combine multiple classifiers or multiple modalities in biometrics. In this paper, optimal decision fusion (ODF) by AND rule and OR rule is presented. We show that the decision fusion can be done in an optimal way such that it always gives an improvement in terms of error rates over the classifiers that are fused. Both the optimal decision fusion theory and the experimental results on the FRGC 2D and 3D face data are given. Experiments show that the optimal decision fusion effectively combines the 2D texture and 3D shape information, and boosts the performance of the system

A Geometric Approach to Pairwise Bayesian Alignment of Functional Data Using Importance Sampling

We present a Bayesian model for pairwise nonlinear registration of functional data. We use the Riemannian geometry of the space of warping functions to define appropriate prior distributions and sample from the posterior using importance sampling. A simple square-root transformation is used to simplify the geometry of the space of warping functions, which allows for computation of sample statistics, such as the mean and median, and a fast implementation of a $k$-means clustering algorithm. These tools allow for efficient posterior inference, where multiple modes of the posterior distribution corresponding to multiple plausible alignments of the given functions are found. We also show pointwise $95\%$ credible intervals to assess the uncertainty of the alignment in different clusters. We validate this model using simulations and present multiple examples on real data from different application domains including biometrics and medicine

A Brief Look into Biometrics and One Use in Higher Education

Biometrics for the purpose of identification is not a new concept, nor is it limited to one specific field. Both physical and biological unique characteristics are being utilized today by biometric technology as a means of recognition (Krishan & Mostafavi, 2018). How exactly are biometrics used today in authorization and identification systems? What are some of the advantages of using biometric technologies over traditional methods of authentication? What are some of the security and privacy concerns of using biometric technology? In this paper, by reviewing multiple published articles in the field of biometrics, we seek to answer these questions, provide insight into the future of biometrics, and discuss the varying responses that biometrics has received from end users, including biometric legislation. We will then look deeper into one particular area of biometric technology, voice recognition, by proposing research in higher education to be conducted on this subject

Biometrics in ABC: counter-spoofing research

Automated border control (ABC) is concerned with fast and secure processing for intelligence-led identification. The FastPass project aims to build a harmonised, modular reference system for future European ABC. When biometrics is taken on board as identity, spoofing attacks become a concern. This paper presents current research in algorithm development for counter-spoofing attacks in biometrics. Focussing on three biometric traits, face, fingerprint, and iris, it examines possible types of spoofing attacks, and reviews existing algorithms reported in relevant academic papers in the area of countering measures to biometric spoofing attacks. It indicates that the new developing trend is fusion of multiple biometrics against spoofing attacks

Technical, Legal, Economic and Social Aspects of Biometrics for Cloud Computing

This article addresses technical, legal, economic and social aspects of biometrics for cloud computing, featuring application example, gains of such solution, current laws, directives and legislation for biometrics and cloud computing. It is primarily based on Slovenian example due to common general EU legislation in the field of cloud computing and biometrics. Authentication on the Internet is still mainly done using passwords, while biometrics is practically not used. It is commonly known that everything is moving to the cloud and biometrics is not an exception. Amount of biometric data is expected to grow significantly over the next few years and only cloud computing is possible to process such amounts of data. Due to these facts and increasing security needs, we propose and implement the use of biometry as a service in the cloud. A challenge regarding the use of biometric solutions in the cloud is the protection of the privacy of individuals and their personal data. In Slovenia privacy legislation is very strong, it permits usage of biometrics only for very specific reasons, but we predict that big players on the market will change this fact globally. One of the important reasons for that is also the fact that biometrics for cloud computing provides some strong benefits and economic incentives. Proper deployment can provide significant savings. Such solutions could improve people’s quality of life in terms of social development, especially in sense of more convenient, safer and reliable identification over multiple government and non-government services

The Day-of-the-Week Effect Revisited: An Alternative Testing Approach

This paper questions traditional approaches for testing the day-of-the-week effect on stock returns. We propose an alternative approach based on the closure test principle introduced by Marcus, Peritz and Gabriel (1976), which has become very popular in Biometrics and Medical Statistics. We test all pairwise comparisons of daily expected stock returns, while the probability of committing any type I error is always kept smaller than or equal to some prespecified level a for each combination of true null hypotheses. We confirm day-of-theweek effects for the S&P 500, the FTSE 30 and the DAX 30 found in earlier studies, but find no evidence for the 1990's.Day-of-the-week effect, Multiple hypotheses testing, Multiple comparisons, Closed test procedures, Multiple level a test