999 resultados para nonintrusive identity verification


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Internet services are important part of daily activities for most of us. These services come with sophisticated authentication requirements which may not be handled by average Internet users. The management of secure passwords for example creates an extra overhead which is often neglected due to usability reasons. Furthermore, password-based approaches are applicable only for initial logins and do not protect against unlocked workstation attacks. In this paper, we provide a non-intrusive identity verification scheme based on behavior biometrics where keystroke dynamics based-on free-text is used continuously for verifying the identity of a user in real-time. We improved existing keystroke dynamics based verification schemes in four aspects. First, we improve the scalability where we use a constant number of users instead of whole user space to verify the identity of target user. Second, we provide an adaptive user model which enables our solution to take the change of user behavior into consideration in verification decision. Next, we identify a new distance measure which enables us to verify identity of a user with shorter text. Fourth, we decrease the number of false results. Our solution is evaluated on a data set which we have collected from users while they were interacting with their mail-boxes during their daily activities.

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This document outlines the system submitted by the Speech and Audio Research Laboratory at the Queensland University of Technology (QUT) for the Speaker Identity Verication: Application task of EVALITA 2009. This submission consisted of a score-level fusion of three component systems, a joint-factor GMM system and two SVM systems using GLDS and GMM supervector kernels. Development and evaluation results are presented, demonstrating the effectiveness of this fused system approach.

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This document outlines the system submitted by the Speech and Audio Research Laboratory at the Queensland University of Technology (QUT) for the Speaker Identity Verification: Application task of EVALITA 2009. This competitive submission consisted of a score-level fusion of three component systems; a joint-factor analysis GMM system and two SVM systems using GLDS and GMM supervector kernels. Development evaluation and post-submission results are presented in this study, demonstrating the effectiveness of this fused system approach. This study highlights the challenges associated with system calibration from limited development data and that mismatch between training and testing conditions continues to be a major source of error in speaker verification technology.

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"Defrauding land titles systems impacts upon us all. Those who deal in land include ordinary citizens, big business, small business, governments, not-for-profit organisation, deceased estates...Fraud here touches almost everybody." the thesis presented in this paper is that the current and disparate steps taken by jurisdictions to alleviate land fraud associated with identity-based crimes are inadequate. The centrepiece of the analysis is the consideration of two scenarios that have recently occurred. One is the typical scenario where a spouse forges the partner's signature to obtain a mortgage from a financial institution. The second is atypical. It involves a sophisticated overseas fraud duping many stakeholders involved in the conveyancing process. After outlining these scenarios, we will examine how identity verification requirements of the United Kingdom, Ontario, the Australian states, and New Zealand would have been applied to these two frauds. Our conclusion is that even though some jurisdictions may have prevented the frauds from occurring, the current requirements are inadequate. We use the lessons learnt to propose what we consider core principles for identity verification in land transactions.

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Iris based identity verification is highly reliable but it can also be subject to attacks. Pupil dilation or constriction stimulated by the application of drugs are examples of sample presentation security attacks which can lead to higher false rejection rates. Suspects on a watch list can potentially circumvent the iris based system using such methods. This paper investigates a new approach using multiple parts of the iris (instances) and multiple iris samples in a sequential decision fusion framework that can yield robust performance. Results are presented and compared with the standard full iris based approach for a number of iris degradations. An advantage of the proposed fusion scheme is that the trade-off between detection errors can be controlled by setting parameters such as the number of instances and the number of samples used in the system. The system can then be operated to match security threat levels. It is shown that for optimal values of these parameters, the fused system also has a lower total error rate.

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Immediate indefeasibility is the cornerstone of the Torrens system of land registration. However, when combined with the apparent ease in which forged mortgages become registered, the operation of this doctrine can come into question. This article seeks to argue that, rather than question indefeasibility, the focus should be on the verification of identity of parties to land transactions. Whilst no system can ever be infallible, it is suggested that by correctly imposing the responsibility for identity verification on the appropriate individual, the Torrens system can retain immediate indefeasibility as its paramount principle, yet achieve the optimum level of fairness in terms of allocation of responsibility and loss. With the dawn of a new era of electronic conveyancing about to begin, the framework suggested here provides a model for minimising the risks of forged mortgages and appropriately allocating the loss.

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There is no doubt that fraud in relation to land transactions is a problem that resonates amongst land academics, practitioners, and stakeholders involved in conveyancing. As each land registration and conveyancing process increasingly moves towards a fully electronic environment, we need to make sure that we understand and guard against the frauds that can occur. What this paper does is examine the types of fraud that have occurred in paper-based conveyancing systems in Australia and considers how they might be undertaken in the National Electronic Conveyancing System (NECS) that is currently under development. Whilst no system can ever be infallible, it is suggested that by correctly imposing the responsibility for identity verification on the appropriate individual, the conveyancing system adopted can achieve the optimum level of fairness in terms of allocation of responsibility and loss. As we sit on the cusp of a new era of electronic conveyancing, the framework suggested here provides a model for minimising the risks of forged mortgages and appropriately allocating the loss. Importantly it also recognises that the electronic environment will see new opportunities for those with criminal intent to undermine the integrity of land transactions. An appreciation of this now, can see the appropriate measures put in place to minimise the risk.

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Statistical dependence between classifier decisions is often shown to improve performance over statistically independent decisions. Though the solution for favourable dependence between two classifier decisions has been derived, the theoretical analysis for the general case of 'n' client and impostor decision fusion has not been presented before. This paper presents the expressions developed for favourable dependence of multi-instance and multi-sample fusion schemes that employ 'AND' and 'OR' rules. The expressions are experimentally evaluated by considering the proposed architecture for text-dependent speaker verification using HMM based digit dependent speaker models. The improvement in fusion performance is found to be higher when digit combinations with favourable client and impostor decisions are used for speaker verification. The total error rate of 20% for fusion of independent decisions is reduced to 2.1% for fusion of decisions that are favourable for both client and impostors. The expressions developed here are also applicable to other biometric modalities, such as finger prints and handwriting samples, for reliable identity verification.

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Reliability of the performance of biometric identity verification systems remains a significant challenge. Individual biometric samples of the same person (identity class) are not identical at each presentation and performance degradation arises from intra-class variability and inter-class similarity. These limitations lead to false accepts and false rejects that are dependent. It is therefore difficult to reduce the rate of one type of error without increasing the other. The focus of this dissertation is to investigate a method based on classifier fusion techniques to better control the trade-off between the verification errors using text-dependent speaker verification as the test platform. A sequential classifier fusion architecture that integrates multi-instance and multisample fusion schemes is proposed. This fusion method enables a controlled trade-off between false alarms and false rejects. For statistically independent classifier decisions, analytical expressions for each type of verification error are derived using base classifier performances. As this assumption may not be always valid, these expressions are modified to incorporate the correlation between statistically dependent decisions from clients and impostors. The architecture is empirically evaluated by applying the proposed architecture for text dependent speaker verification using the Hidden Markov Model based digit dependent speaker models in each stage with multiple attempts for each digit utterance. The trade-off between the verification errors is controlled using the parameters, number of decision stages (instances) and the number of attempts at each decision stage (samples), fine-tuned on evaluation/tune set. The statistical validation of the derived expressions for error estimates is evaluated on test data. The performance of the sequential method is further demonstrated to depend on the order of the combination of digits (instances) and the nature of repetitive attempts (samples). The false rejection and false acceptance rates for proposed fusion are estimated using the base classifier performances, the variance in correlation between classifier decisions and the sequence of classifiers with favourable dependence selected using the 'Sequential Error Ratio' criteria. The error rates are better estimated by incorporating user-dependent (such as speaker-dependent thresholds and speaker-specific digit combinations) and class-dependent (such as clientimpostor dependent favourable combinations and class-error based threshold estimation) information. The proposed architecture is desirable in most of the speaker verification applications such as remote authentication, telephone and internet shopping applications. The tuning of parameters - the number of instances and samples - serve both the security and user convenience requirements of speaker-specific verification. The architecture investigated here is applicable to verification using other biometric modalities such as handwriting, fingerprints and key strokes.

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In recent years a great deal of case law has been generated in relation to mortgages where the mortgagee has not engaged in adequate identity verification of the mortgagor and the mortgage has subsequently been found to be forged. As a result, careless mortgagee provisions operate in Queensland as an exception to indefeasibility. Similar provisions are expected to commence soon in New South Wales. This article examines the mortgagee’s position with the benefit of indefeasibility and then considers the impact of the careless mortgagee provisions on the rights of a mortgagee under a forged mortgage, concluding that the provisions significantly change the dynamic between a registered mortgagee and registered owner who has not signed the mortgage. These provisions appear to give the mortgagee a conditional indefeasibility, with the intention of reducing the State’s exposure to the payment of compensation in the case of identity fraud. They are however, more successful in the case of forgery by a third party rather than forgery by a co-owner.

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The thesis presented in this paper is that the land fraud committed by Matthew Perrin in Queensland and inflicted upon Roger Mildenhall in Western Australia demonstrates the need for urgent procedural reform to the conveyancing process. Should this not occur, then calls to reform the substantive principles of the Torrens system will be heard throughout the jurisdictions that adopt title by registration, particularly in those places where immediate indefeasibility is still the norm. This paper closely examines the factual matrix behind both of these frauds, and asks what steps should have been taken to prevent them occurring. With 2012 bringing us Australian legislation embedding a national e-conveyancing system and a new Land Transfer Act for New Zealand we ask what legislative measures should be introduced to minimise the potential for such fraud. In undertaking this study, we reflect on whether the activities of Perrin and the criminals responsible for stealing Mildenhall's land would have succeeded under the present system for automated registration utilised in New Zealand.

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Classifier selection is a problem encountered by multi-biometric systems that aim to improve performance through fusion of decisions. A particular decision fusion architecture that combines multiple instances (n classifiers) and multiple samples (m attempts at each classifier) has been proposed in previous work to achieve controlled trade-off between false alarms and false rejects. Although analysis on text-dependent speaker verification has demonstrated better performance for fusion of decisions with favourable dependence compared to statistically independent decisions, the performance is not always optimal. Given a pool of instances, best performance with this architecture is obtained for certain combination of instances. Heuristic rules and diversity measures have been commonly used for classifier selection but it is shown that optimal performance is achieved for the `best combination performance' rule. As the search complexity for this rule increases exponentially with the addition of classifiers, a measure - the sequential error ratio (SER) - is proposed in this work that is specifically adapted to the characteristics of sequential fusion architecture. The proposed measure can be used to select a classifier that is most likely to produce a correct decision at each stage. Error rates for fusion of text-dependent HMM based speaker models using SER are compared with other classifier selection methodologies. SER is shown to achieve near optimal performance for sequential fusion of multiple instances with or without the use of multiple samples. The methodology applies to multiple speech utterances for telephone or internet based access control and to other systems such as multiple finger print and multiple handwriting sample based identity verification systems.

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New Zealand and Australia are leading the world in terms of automated land registry systems. Landonline was introduced some ten years ago for New Zealand, and the Electronic Conveyancing National Law (ECNL) is to be released over the next few years in support of a national electronic conveyancing system to be used throughout Australia. With the assistance of three proof requirements, developed for this purpose, this article measures the integrity of both systems as against the old, manual Torrens system. The authors take the position that any introduced system should at least have the same level of integrity and safety as the originally conceived manual system. The authors argue both Landonline and ECNL, as presently set up, have less credibility than the manual system as it was designed to operate, leading to the possibility of increased fraud or misuse.

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Texture information in the iris image is not uniform in discriminatory information content for biometric identity verification. The bits in an iris code obtained from the image differ in their consistency from one sample to another for the same identity. In this work, errors in bit strings are systematically analysed in order to investigate the effect of light-induced and drug-induced pupil dilation and constriction on the consistency of iris texture information. The statistics of bit errors are computed for client and impostor distributions as functions of radius and angle. Under normal conditions, a V-shaped radial trend of decreasing bit errors towards the central region of the iris is obtained for client matching, and it is observed that the distribution of errors as a function of angle is uniform. When iris images are affected by pupil dilation or constriction the radial distribution of bit errors is altered. A decreasing trend from the pupil outwards is observed for constriction, whereas a more uniform trend is observed for dilation. The main increase in bit errors occurs closer to the pupil in both cases.