Authenticating the identity of computer users with typing biometrics and the fuzzy min-max neural network

In this paper, typing biometrics is applied as an additional security measure to the password-based or Personal Identification Number (PIN)-based systems to authenticate the identity of computer users. In particular, keystroke pressure and latency signals are analyzed using the Fuzzy Min-Max (FMM) neural network for authentication purposes. A special pressure-sensitive keyboard is designed to collect keystroke pressure signals, in addition to the latency signals, from computer users when they type their passwords. Based on the keystroke pressure and latency signals, the FMM network is employed to classify the computer users into two categories, i.e., genuine users or impostors. To assess the effectiveness of the proposed approach, two sets of experiments are conducted, and the results are compared with those from statistical methods and neural network models. The experimental outcomes positively demonstrate the potentials of using typing biometrics and the FMM network to provide an additional security layer for the current password-based or PIN-based methods in authenticating the identity of computer users.

Keystroke patterns classification using the ARTMAP-FD neural network

This paper presents the development of a keystroke dynamics-based user authentication system using the ARTMAP-FD neural network. The effectiveness of ARTMAPFD in classifying keystroke patterns is analyzed and compared against a number of widely used machine learning systems. The results show that ARTMAP-FD performs well against many of its counterparts in keystroke patterns classification. Apart from that, instead of using the conventional typing timing characteristics, the applicability of typing pressure to ascertaining user's identity is investigated. The experimental results show that combining both latency and pressure patterns can improve the Equal Error Rate (ERR) of the system.

Soft-biometrics : unconstrained authentication in a surveillance environment

Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. These include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics (i.e. face, voice) which require cooperation from the subject, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. Whilst these traits cannot provide robust authentication, they can be used to provide coarse authentication or identification at long range, locate a subject who has been previously seen or who matches a description, as well as aid in object tracking. In this paper we propose three part (head, torso, legs) height and colour soft biometric models, and demonstrate their verification performance on a subset of the PETS 2006 database. We show that these models, whilst not as accurate as traditional biometrics, can still achieve acceptable rates of accuracy in situations where traditional biometrics cannot be applied.

Regulating IVF and pre-implantation tissue-typing for the creation of "saviour siblings" : a harm analysis

Scientific discoveries, developments in medicine and health issues are the constant focus of media attention and the principles surrounding the creation of so called ‘saviour siblings’ are of no exception. The development in the field of reproductive techniques has provided the ability to genetically analyse embryos created in the laboratory to enable parents to implant selected embryos to create a tissue-matched child who may be able to cure an existing sick child. The research undertaken in this thesis examines the regulatory frameworks overseeing the delivery of assisted reproductive technologies (ART) in Australia and the United Kingdom and considers how those frameworks impact on the accessibility of in vitro fertilisation (IVF) procedures for the creation of ‘saviour siblings’. In some jurisdictions, the accessibility of such techniques is limited by statutory requirements. The limitations and restrictions imposed by the state in relation to the technology are analysed in order to establish whether such restrictions are justified. The analysis is conducted on the basis of a harm framework. The framework seeks to establish whether those affected by the use of the technology (including the child who will be created) are harmed. In order to undertake such evaluation, the concept of harm is considered under the scope of John Stuart Mill’s liberal theory and the Harm Principle is used as a normative tool to judge whether the level of harm that may result, justifies state intervention or restriction with the reproductive decision-making of parents in this context. The harm analysis conducted in this thesis seeks to determine an appropriate regulatory response in relation to the use of pre-implantation tissue-typing for the creation of ‘saviour siblings’. The proposals outlined in the last part of this thesis seek to address the concern that harm may result from the practice of pre-implantation tissue-typing. The current regulatory frameworks in place are also analysed on the basis of the harm framework established in this thesis. The material referred to in this thesis reflects the law and policy in place in Australia and the UK at the time the thesis was submitted for examination (December 2009).

On model typing

Where object-oriented languages deal with objects as described by classes, model-driven development uses models, as graphs of interconnected objects, described by metamodels. A number of new languages have been and continue to be developed for this model- based paradigm, both for model transformation and for general programming using models. Many of these use single-object approaches to typing, derived from solutions found in object-oriented systems, while others use metamodels as model types, but without a clear notion of polymorphism. Both of these approaches lead to brittle and overly restrictive reuse characteristics. In this paper we propose a simple extension to object-oriented typing to better cater for a model-oriented context, including a simple strategy for typing models as a collection of interconnected objects. We suggest extensions to existing type system formalisms to support these concepts and their manipulation. Using a simple example we show how this extended approach permits more flexible reuse, while preserving type safety.

Super-resolved faces for improved face recognition from surveillance video advances in biometrics

Characteristics of surveillance video generally include low resolution and poor quality due to environmental, storage and processing limitations. It is extremely difficult for computers and human operators to identify individuals from these videos. To overcome this problem, super-resolution can be used in conjunction with an automated face recognition system to enhance the spatial resolution of video frames containing the subject and narrow down the number of manual verifications performed by the human operator by presenting a list of most likely candidates from the database. As the super-resolution reconstruction process is ill-posed, visual artifacts are often generated as a result. These artifacts can be visually distracting to humans and/or affect machine recognition algorithms. While it is intuitive that higher resolution should lead to improved recognition accuracy, the effects of super-resolution and such artifacts on face recognition performance have not been systematically studied. This paper aims to address this gap while illustrating that super-resolution allows more accurate identification of individuals from low-resolution surveillance footage. The proposed optical flow-based super-resolution method is benchmarked against Baker et al.’s hallucination and Schultz et al.’s super-resolution techniques on images from the Terrascope and XM2VTS databases. Ground truth and interpolated images were also tested to provide a baseline for comparison. Results show that a suitable super-resolution system can improve the discriminability of surveillance video and enhance face recognition accuracy. The experiments also show that Schultz et al.’s method fails when dealing surveillance footage due to its assumption of rigid objects in the scene. The hallucination and optical flow-based methods performed comparably, with the optical flow-based method producing less visually distracting artifacts that interfered with human recognition.

Determining operational measures from multi-camera surveillance systems using soft biometrics

CCTV and surveillance networks are increasingly being used for operational as well as security tasks. One emerging area of technology that lends itself to operational analytics is soft biometrics. Soft biometrics can be used to describe a person and detect them throughout a sparse multi-camera network. This enables them to be used to perform tasks such as determining the time taken to get from point to point, and the paths taken through an environment by detecting and matching people across disjoint views. However, in a busy environment where there are 100's if not 1000's of people such as an airport, attempting to monitor everyone is highly unrealistic. In this paper we propose an average soft biometric, that can be used to identity people who look distinct, and are thus suitable for monitoring through a large, sparse camera network. We demonstrate how an average soft biometric can be used to identify unique people to calculate operational measures such as the time taken to travel from point to point.

Regulating assisted reproductive technologies in Victoria : the impact of changing policy concerning the accessibility of in vitro fertilisation for preimplantation tissue-typing

On 1 January 2010, the Assisted Reproductive Treatment Act 2008 (Vic) came into force. The legislation was the outcome of a detailed review and consultation process undertaken by the Victorian Law Reform Commission. Arguably, the change to the regulatory framework represents a significant shift in policy compared to previous regulatory approaches on this topic in Victoria. This article considers the impact of the new legislation on eligibility for reproductive treatments, focusing on the accessibility of such services for the purpose of creating a “saviour sibling”. It also highlights the impact of the Victorian regulatory body’s decision to abolish its regulatory policies on preimplantation genetic diagnosis and preimplantation tissue-typing, concluding that the regulatory approach in relation to these latter issues is similar to other Australian jurisdictions where such practices are not addressed by a statutory framework.

Identifying customer behaviour and dwell time using soft biometrics

In a commercial environment, it is advantageous to know how long it takes customers to move between different regions, how long they spend in each region, and where they are likely to go as they move from one location to another. Presently, these measures can only be determined manually, or through the use of hardware tags (i.e. RFID). Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. They include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. While these traits cannot provide robust authentication, they can be used to provide identification at long range, and aid in object tracking and detection in disjoint camera networks. In this chapter we propose using colour, height and luggage soft biometrics to determine operational statistics relating to how people move through a space. A novel average soft biometric is used to locate people who look distinct, and these people are then detected at various locations within a disjoint camera network to gradually obtain operational statistics

Preliminary validation of a novel high-resolution melt-based typing method based on the multilocus sequence typing scheme of Streptococcus pyogenes

The major limitation of current typing methods for Streptococcus pyogenes, such as emm sequence typing and T typing, is that these are based on regions subject to considerable selective pressure. Multilocus sequence typing (MLST) is a better indicator of the genetic backbone of a strain but is not widely used due to high costs. The objective of this study was to develop a robust and cost-effective alternative to S. pyogenes MLST. A 10-member single nucleotide polymorphism (SNP) set that provides a Simpson’s Index of Diversity (D) of 0.99 with respect to the S. pyogenes MLST database was derived. A typing format involving high-resolution melting (HRM) analysis of small fragments nucleated by each of the resolution-optimized SNPs was developed. The fragments were 59–119 bp in size and, based on differences in G+C content, were predicted to generate three to six resolvable HRM curves. The combination of curves across each of the 10 fragments can be used to generate a melt type (MelT) for each sequence type (ST). The 525 STs currently in the S. pyogenes MLST database are predicted to resolve into 298 distinct MelTs and the method is calculated to provide a D of 0.996 against the MLST database. The MelTs are concordant with the S. pyogenes population structure. To validate the method we examined clinical isolates of S. pyogenes of 70 STs. Curves were generated as predicted by G+C content discriminating the 70 STs into 65 distinct MelTs.

An examination of keystroke dynamics for continuous user authentication

Most current computer systems authorise the user at the start of a session and do not detect whether the current user is still the initial authorised user, a substitute user, or an intruder pretending to be a valid user. Therefore, a system that continuously checks the identity of the user throughout the session is necessary without being intrusive to end-user and/or effectively doing this. Such a system is called a continuous authentication system (CAS). Researchers have applied several approaches for CAS and most of these techniques are based on biometrics. These continuous biometric authentication systems (CBAS) are supplied by user traits and characteristics. One of the main types of biometric is keystroke dynamics which has been widely tried and accepted for providing continuous user authentication. Keystroke dynamics is appealing for many reasons. First, it is less obtrusive, since users will be typing on the computer keyboard anyway. Second, it does not require extra hardware. Finally, keystroke dynamics will be available after the authentication step at the start of the computer session. Currently, there is insufficient research in the CBAS with keystroke dynamics field. To date, most of the existing schemes ignore the continuous authentication scenarios which might affect their practicality in different real world applications. Also, the contemporary CBAS with keystroke dynamics approaches use characters sequences as features that are representative of user typing behavior but their selected features criteria do not guarantee features with strong statistical significance which may cause less accurate statistical user-representation. Furthermore, their selected features do not inherently incorporate user typing behavior. Finally, the existing CBAS that are based on keystroke dynamics are typically dependent on pre-defined user-typing models for continuous authentication. This dependency restricts the systems to authenticate only known users whose typing samples are modelled. This research addresses the previous limitations associated with the existing CBAS schemes by developing a generic model to better identify and understand the characteristics and requirements of each type of CBAS and continuous authentication scenario. Also, the research proposes four statistical-based feature selection techniques that have highest statistical significance and encompasses different user typing behaviors which represent user typing patterns effectively. Finally, the research proposes the user-independent threshold approach that is able to authenticate a user accurately without needing any predefined user typing model a-priori. Also, we enhance the technique to detect the impostor or intruder who may take over during the entire computer session.