1,720,992 research outputs found
An experimental analysis of the relationship between biometric template update and the Doddington’s Zoo in face verification
No full textThe problem of biometric template representativeness has recently attracted much attention with the introduction of several template update methods. Automatic template update methods adapt themselves to the intra-class variations of the input data. However, it is possible to hypothesize that the effect of template updating may not be the same for all the clients due to different characteristics of clients present in the biometric database. The goal of this paper is to investigate this hypothesis by explicitly partitioning clients into different groups of the “Doddington’s zoo” as a function of their “intrinsic” characteristics, and studying the effect of state of art template “self update” procedure on these different groups. Experimental evaluation on Equinox database with a case study on face verification system based on EBGM algorithm shows the strong evidence of non-uniform update effects on different clients classes and suggest to modify the update procedures according to the client’s characteristics
Boosting Gallery Representativeness by Co-updating Face and Fingerprint Verification Systems
No full textAbstract. The representativeness of a template gallery to the novel data is a
well-known issue in a personal verification system based on biometrics. This
problem has been recently faced by proposing “template update” algorithms
that updates the enrolled templates in order to capture and represent better, the
subject’s intra-class variations. Whilst the majority of the proposed approaches
adopted “self” update technique, in which the system updates itself using its
own knowledge. An approach named template co-update, using two
complementary biometrics to “co-update” each other, has shown promising, but
still preliminary, results. In this paper, we investigate the performance of the
template co-update in comparison to self update algorithms in an uncontrolled
environment. Reported results show that template co-update can outperform
template “self” update technique, when initial enrolled templates are poor
representative of the novel data
Biometric template update: an experimental investigation on the relationship between update errors and performance degradation in face verification
No full textCurrent methods for automatic template update are aimed at capturing large intra-class variations of input data and at the same time restricting the probability of impostor’s introduction in client’s galleries. These automatic methods avoid the costs of supervised update methods, which are due to repeated enrollment sessions and manual assignment of identity labels. Most of state-of-the-art template update approaches add input patterns to the claimed identity’s gallery on the basis of their matching score with the existing templates, which must be above a very high “updating” threshold. However, regardless of the value of such updating threshold, update errors do exist and impact strongly on the effectiveness of update procedures. The introduction of impostors into the galleries may degrade the performance quickly. This effect has not been studied in the literature so far. Therefore, a first experimental investigation is the goal of this paper, with a case study on a face verification system
Biometric template update: An experimental investigation on the relationship between update errors and performance degradation in face verification
No full textCurrent methods for automatic template update are aimed at capturing large intra-class variations of input data and at the same time restricting the probability of impostor's introduction in client's galleries. These automatic methods avoid the costs of supervised update methods, which are due to repeated enrollment sessions and manual assignment of identity labels. Most of state-of-the-art template update approaches add input patterns to the claimed identity's gallery on the basis of their matching score with the existing templates, which must be above a very high "updating" threshold. However, regardless of the value of such updating threshold, update errors do exist and impact strongly on the effectiveness of update procedures. The introduction of impostors into the galleries may degrade the performance quickly. This effect has not been studied in the literature so far. Therefore, a first experimental investigation is the goal of this paper, with a case study on a face verification system. © 2008 Springer Berlin Heidelberg
Boosting Gallery Representativeness by Co-updating Face and Fingerprint Verification Systems
No full textAbstract. The representativeness of a template gallery to the novel data is a
well-known issue in a personal verification system based on biometrics. This
problem has been recently faced by proposing ?template update? algorithms
that updates the enrolled templates in order to capture and represent better, the
subject?s intra-class variations. Whilst the majority of the proposed approaches
adopted ?self? update technique, in which the system updates itself using its
own knowledge. An approach named template co-update, using two
complementary biometrics to ?co-update? each other, has shown promising, but
still preliminary, results. In this paper, we investigate the performance of the
template co-update in comparison to self update algorithms in an uncontrolled
environment. Reported results show that template co-update can outperform
template ?self? update technique, when initial enrolled templates are poor
representative of the novel data
Adaptive Multibiometric Systems: Conceptual Representation and Performance Evaluation Over Time
No full textGroup-specific score normalization for biometric systems
No full textThe problem of biometric menagerie, first pointed out by Doddington et al. (1998), is one that plagues all biometric systems. They observe that only a handful of clients (enrolled users in the gallery) actually contribute disproportionately to recognition errors. While prior literature attempting to reduce this effect focuses on either client-specific score normalization or client-specific decision strategies, in this study, we explore a novel category of approaches: group-specific score normalization. While client-specific score normalization can be negatively impacted by the paucity of genuine score samples, group-specific score normalization is less affected since the matching score samples of different clients belonging to the same group are aggregated. Experimental evidence based on face, fingerprint and iris modalities show that our proposal generally outperforms client-specific score normalization as well as the baseline systems (without any normalization) across all possible operating points (so obtained by changing the decision threshold)
Investigating the Usability of SIFT Features in Biometrics
No full textRecent advancements of biometrics identity verification are growing rapidly in this vastly interconnected techno-savvy society. In this information age, protection of valuable contents from the unauthorised intruders or illegal entry to high security zones has made these biometric systems crucial mechanism towards establishing a robust identity verification system. The thrust for reliable authentication methodologies are increasing due to security consciousness of people and also for growing advancement of civilian infrastructures by means of networking, communication, E-Governance, IT knowledge-based civic environment, etc. In the last two decades, a large number of computational intelligence (CI) based and non-linear synchronization based approaches have been thoroughly investigated in biometric authentication in terms of automatic feature detection, feature matching and association of adaptive parameters to the system. Although, it has been felt that the robust and invariant ways are necessary to process the system development from one biometric application to another. However, some incapable and negative constraints have made these biometric systems lack of inconvenience to a large group of end users. To cope up with these incapable factors in biometric systems successfully, Scale Invariant Feature Transform (SIFT) operator has been thoroughly investigated and proved to be invariant to image rotation, scaling, partly illumination changes, biometric authentication towards efficient identity verification
A dual-staged classification-selection approach for automated update of biometric templates
No full textIn the emerging field of adaptive biometrics, systems aim to adapt enrolled templates to variations in samples observed during operations. However, despite numerous advantages, few commercial vendors have adopted auto-update procedures in their products. This is due to limitations associated with existing adaptation schemes. This paper proposes a dual-staged template adaptation scheme that allows to capture `informative' operational samples with significant variations but without increasing the vulnerability to impostor intrusion. This is achieved through a two staged classification-selection approach driven by the harmonic function and risk minimization technique, over a graph based representation of (enrolment and operational) samples. Experimental results on the DIEE fingerprint data set, explicitly collected for evaluating adaptive biometric systems, demonstrate that the proposed scheme results in 67% reduction in error over the baseline system (without adaptation), outperforming state-of-the-art method
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