The Biometric Menagerie

Yager, Neil; Dunstone, Ted

doi:10.1109/tpami.2008.291

Cited by 143 publications

(120 citation statements)

References 13 publications

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“…These factors also comprise the intrinsic characteristics of each user which include users particularly vulnerable to impersonation or users difficult to match, among others. The literature refer to this as biometric zoo [16] or menagerie [17]. -Sensor factors: related with the sensor, human-machine interactions, ease of use or maintenance.…”

Section: Quality Assessment For Keystroke Dynamicsmentioning

confidence: 99%

“…4b). The researchers analyzed and defined these classes of users as the biometric menagerie [17] or biometric zoo [16].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Inspired by the methodology employed in [17], we divided the population in three groups according to their performance (obtained following the experimental protocol explained in Section 3.2) as Good (33% of users with lowest EER), Ugly (33% of users with highest EER) and Bad (the remaining 33% of users). This is not an ideal classification but allows us to group users with similar performances.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics

Morales

Fiérrez

Ortega‐Garcia

2015

Computer Vision - ECCV 2014 Workshops

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El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Abstract. This paper studies ways to detect good users for biometric recognition based on keystroke dynamics. Keystroke dynamics is an active research field for the biometric scientific community. Despite the great efforts made during the last decades, the performance of keystroke dynamics recognition systems is far from the performance achieved by traditional hard biometrics. This is very pronounced for some users, who generate many recognition errors even with the most sophisticate recognition algorithms. On the other hand, previous works have demonstrated that some other users behave particularly well even with the simplest recognition algorithms. Our purpose here is to study ways to distinguish such classes of users using only the genuine enrollment data. The experiments comprise a public database and two popular recognition algorithms. The results show the effectiveness of the Kullback-Leibler divergence as a quality measure to categorize users in comparison with other four statistical measures.

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Section: Quality Assessment For Keystroke Dynamicsmentioning

confidence: 99%

“…4b). The researchers analyzed and defined these classes of users as the biometric menagerie [17] or biometric zoo [16].…”

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics

Morales

Fiérrez

Ortega‐Garcia

2015

Computer Vision - ECCV 2014 Workshops

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“…8). PSEI spans the analogue of a biometric/forensic menagerie [50]. It expands on the type of attacks and also addresses covariate shift (e.g., changes in the data distributions characteristic of the arm race encountered during evolution) on one side, and change and drift detection, on the other side.…”

Section: Immunity and Detectionmentioning

confidence: 99%

Cyberspace Security Using Adversarial Learning and Conformal Prediction

Wechsler¹

2015

IIM

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This paper advances new directions for cyber security using adversarial learning and conformal prediction in order to enhance network and computing services defenses against adaptive, malicious, persistent, and tactical offensive threats. Conformal prediction is the principled and unified adaptive and learning framework used to design, develop, and deploy a multi-faceted self-managing defensive shield to detect, disrupt, and deny intrusive attacks, hostile and malicious behavior, and subterfuge. Conformal prediction leverages apparent relationships between immunity and intrusion detection using non-conformity measures characteristic of affinity, a typicality, and surprise, to recognize patterns and messages as friend or foe and to respond to them accordingly. The solutions proffered throughout are built around active learning, meta-reasoning, randomness, distributed semantics and stratification, and most important and above all around adaptive Oracles. The motivation for using conformal prediction and its immediate off-spring, those of semi-supervised learning and transduction, comes from them first and foremost supporting discriminative and non-parametric methods characteristic of principled demarcation using cohorts and sensitivity analysis to hedge on the prediction outcomes including negative selection, on one side, and providing credibility and confidence indices that assist meta-reasoning and information fusion.

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“…It is method in which user uses body for authentication. It uses psychological or behavioral characteristic like facial or fingerprints scans and iris or voice recognition to identify users [1]. For example retina scans or finger prints.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Method for Graphical Password Mechanism

Siddharth¹

2015

IJRITCC

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Abstract-For the verification of authorized users in computer systems, various text based or biometrics methods are used. But these methods have some drawbacks. It is difficult to remember and recall the textual i.e. alphanumeric passwords. To avoid this drawback users prefer to create effortless, short, easy and insecure passwords which are easily guessable by hacker and this makes the system more vulnerable to attacks. On other hand, verification mechanisms based on biometrics offers security to a good extents. But they are quite luxurious for implementation. Cost becomes a key factor in the case of biometrics. Also any injury to the body part used in biometric authorization results in denial of access or performance issues. Graphical password provides another way by providing passwords that are more protected and unforgettable in a reasonable price. In this system, user clicks on images instead of typing passwords for accessing the system. This paper describes and examines usability and security of graphical password mechanism for authentication using graphical passwords. Proposed system describes characteristics for security and performed empirical study comparing Graphical password mechanism with Biometric passwords and alphanumeric password.

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The Biometric Menagerie

Cited by 143 publications

References 13 publications

Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics

Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics

Cyberspace Security Using Adversarial Learning and Conformal Prediction

A Novel Method for Graphical Password Mechanism

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