Design and implementation of a multi-modal biometric system for company access control

This paper is about the design, implementation, and deployment of a multi-modal biometric system to grant access to a company structure and to internal zones in the company itself. Face and iris have been chosen as biometric traits. Face is feasible for non-intrusive checking with a minimum cooperation from the subject, while iris supports very accurate recognition procedure at a higher grade of invasivity. The recognition of the face trait is based on the Local Binary Patterns histograms, and the Daughman\u2019s method is implemented for the analysis of the iris data. The recognition process may require either the acquisition of the user\u2019s face only or the serial acquisition of both the user\u2019s face and iris, depending on the confidence level of the decision with respect to the set of security levels and requirements, stated in a formal way in the Service Level Agreement at a negotiation phase. The quality of the decision depends on the setting of proper different thresholds in the decision modules for the two biometric traits. Any time the quality of the decision is not good enough, the system activates proper rules, which ask for new acquisitions (and decisions), possibly with different threshold values, resulting in a system not with a fixed and predefined behaviour, but one which complies with the actual acquisition context. Rules are formalized as deduction rules and grouped together to represent \u201cresponse behaviors\u201d according to the previous analysis. Therefore, there are different possible working flows, since the actual response of the recognition process depends on the output of the decision making modules that compose the system. Finally, the deployment phase is described, together with the results from the testing, based on the AT&T Face Database and the UBIRIS database

Pseudo Identities Based on Fingerprint Characteristics

This paper presents the integrated project TURBINE which is funded under the EU 7th research framework programme. This research is a multi-disciplinary effort on privacy enhancing technology, combining innovative developments in cryptography and fingerprint recognition. The objective of this project is to provide a breakthrough in electronic authentication for various applications in the physical world and on the Internet. On the one hand it will provide secure identity verification thanks to fingerprint recognition. On the other hand it will reliably protect the biometric data through advanced cryptography technology. In concrete terms, it will provide the assurance that (i) the data used for the authentication, generated from the fingerprint, cannot be used to restore the original fingerprint sample, (ii) the individual will be able to create different "pseudo-identities" for different applications with the same fingerprint, whilst ensuring that these different identities (and hence the related personal data) cannot be linked to each other, and (iii) the individual is enabled to revoke an biometric identifier (pseudo-identity) for a given application in case it should not be used anymore

Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets

A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favorably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity, this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

On Acquisition and Analysis of a Dataset Comprising of Gait, Ear and Semantic data

In outdoor scenarios such as surveillance where there is very little control over the environments, complex computer vision algorithms are often required for analysis. However constrained environments, such as walkways in airports where the surroundings and the path taken by individuals can be controlled, provide an ideal application for such systems. Figure 1.1 depicts an idealised constrained environment. The path taken by the subject is restricted to a narrow path and once inside is in a volume where lighting and other conditions are controlled to facilitate biometric analysis. The ability to control the surroundings and the flow of people greatly simplifes the computer vision task, compared to typical unconstrained environments. Even though biometric datasets with greater than one hundred people are increasingly common, there is still very little known about the inter and intra-subject variation in many biometrics. This information is essential to estimate the recognition capability and limits of automatic recognition systems. In order to accurately estimate the inter- and the intra- class variance, substantially larger datasets are required [40]. Covariates such as facial expression, headwear, footwear type, surface type and carried items are attracting increasing attention; although considering the potentially large impact on an individuals biometrics, large trials need to be conducted to establish how much variance results. This chapter is the first description of the multibiometric data acquired using the University of Southampton's Multi-Biometric Tunnel [26, 37]; a biometric portal using automatic gait, face and ear recognition for identification purposes. The tunnel provides a constrained environment and is ideal for use in high throughput security scenarios and for the collection of large datasets. We describe the current state of data acquisition of face, gait, ear, and semantic data and present early results showing the quality and range of data that has been collected. The main novelties of this dataset in comparison with other multi-biometric datasets are: 1. gait data exists for multiple views and is synchronised, allowing 3D reconstruction and analysis; 2. the face data is a sequence of images allowing for face recognition in video; 3. the ear data is acquired in a relatively unconstrained environment, as a subject walks past; and 4. the semantic data is considerably more extensive than has been available previously. We shall aim to show the advantages of this new data in biometric analysis, though the scope for such analysis is considerably greater than time and space allows for here

Deployment of Keystroke Analysis on a Smartphone

The current security on mobile devices is often limited to the Personal Identification Number (PIN), a secretknowledge based technique that has historically demonstrated to provide ineffective protection from misuse. Unfortunately, with the increasing capabilities of mobile devices, such as online banking and shopping, the need for more effective protection is imperative. This study proposes the use of two-factor authentication as an enhanced technique for authentication on a Smartphone. Through utilising secret-knowledge and keystroke analysis, it is proposed a stronger more robust mechanism will exist. Whilst keystroke analysis using mobile devices have been proven effective in experimental studies, these studies have only utilised the mobile device for capturing samples rather than the more computationally challenging task of performing the actual authentication. Given the limited processing capabilities of mobile devices, this study focuses upon deploying keystroke analysis to a mobile device utilising numerous pattern classifiers. Given the trade-off with computation versus performance, the results demonstrate that the statistical classifiers are the most effective

Large-scale Biometrics Deployment in Europe: Identifying Challenges and Threats

With large-scale biometrics deployment in the EU still in its infancy and with stakeholders racing to position themselves in view of the lucrative market that is forecasted, a study to identify challenges and threats that need to be dealt with was launched. This is the result: a report on Biometrics large-scale Deployment in Europe. The report tackles three main issues namely, the status, security / privacy and testing / certification processes. A survey was launched so as to help reveal the actual status of Biometrics large-scale Deployment initiatives in EU. The main outcome of the survey was that an open dissemination of implementation results policy is needed mainly on deployment plans, strategies, barriers and best practices. The security/ privacy challenges study identified a number of issues, the most important of which were related to proportionality and compliance to the existing regulatory framework while at the same time it revealed an important number of related actions aiming at ensuring both data security and privacy. The aim of the Bio Testing Europe study was double: to identify and collect comparable and certified results under different technologies, vendors and environments situations and to feed in this information to animate discussion among the members of a European network which would enhance the European testing and certification capacity. The study presents an integrated picture of the identified issues as well as a number of recommendations. With some of the systems that are being implemented involving millions of individuals as target users it is important for policy makers to adopt some of the options presented so as to address the identified through the study challengesJRC.J.4-Information Societ