A Survey of Iris Biometrics Research: 2008–2010

Bowyer, Kevin W.; Hollingsworth, Karen; Flynn, Patrick J.

doi:10.1007/978-1-4471-4402-1_2

Cited by 94 publications

(43 citation statements)

References 198 publications

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“…It is clear, that such large-scale applications demand high accuracy to avoid misclassification. Furthermore, the discrepancy between users aware of the acquisition and the observed decreased rate when applied in unconstrained scenarios with reported VR (verification rate) as low as 44.6% [14] versus >99% VR at 0.1% FAR (false acceptance rate) for a series of iris biometric systems in constrained environments [1] support the claimed need for higher accuracy in less constrained applications.…”

Section: Introductionmentioning

confidence: 94%

Fusion of Iris Segmentation Results

Uhl

Wild

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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While combining more than one biometric sample, recognition algorithm, modality or sensor, commonly referred to as multibiometrics, is common practice to improve accuracy of biometric systems, fusion at segmentation level has so far been neglected in literature. This paper introduces the concept of multi-segmentation fusion for combining independent iris segmentation results. Fusion at segmentation level is useful to (1) obtain more robust recognition rates compared to single segmentation; (2) avoid additional storage requirements compared to feature-level fusion, and (3) save processing time compared to employing parallel chains of feature-extractor dependent segmentation. As proof of concept, manually labeled segmentation results are combined using the proposed technique and shown to increase recognition accuracy for representative algorithms on the well-known CASIA-V4-Interval dataset.

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Section: Introductionmentioning

confidence: 94%

Fusion of Iris Segmentation Results

Uhl

Wild

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“… da íris (Daugman, 2004;Bowyer, Hollingsworth, & Flynn, 2013): uma técnica que se baseia na extração de características da textura da íris; ela é interessante por apresentar 42 boa acurácia; necessita de equipamentos específicos para gerar imagens dos olhos e possui limitações quanto a movimentação da cabeça e da pálpebra;  da face (Parkhi, Vedaldi, & Zisserman, 2015;Jafri & Arabnia, 2009): busca identificar pessoas por diferentes características ligadas a geometria da face e outras particularidades; sua precisão é boa e tem crescido, principalmente com a evolução dos dispositivos fotográficos e das técnicas de modo geral; necessita de dispositivos para capturar imagens e é intrusiva;  da voz (Padmanabhan & Premkumar, 2015): identifica pessoas através do padrão de voz; apresenta boa acurácia em ambientes controlados; porém, pode ter problemas com ruídos sonoros e distância do microfone, além de ser intrusiva;  da digital dos dedos e mãos (Ali, Mahale, Yannawar, & Gaikwad, 2016;Maio, Maltoni, Cappelli, Wayman, & Jain, 2002): o reconhecimento de digitais é amplamente utilizado; possui boa acurácia, porém, necessita de hardware específico para capturar as digitais.…”

Section: Reconhecimento De Usuáriounclassified

Dinâmica da Digitação Aplicada à Autenticação Periódica de Usuários em Ambientes Virtuais de Aprendizagem

Cruz

Duarte

Goldschmidt

2017

RBIE

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Resumo A autenticação de usuário nos ambientes virtuais de aprendizagem (AVAs) existentes é, em geral, pontual e intrusiva, ocorrendo no momento em que o usuário Abstract The authentication of users on a Virtual Learning Environment (VLE) is, in general, punctual and intrusive, occurring when the user connects to the environment, by typing his password. Such approach allows, after the initial login, that unauthenticated users take the role of authenticated users and perform tasks in the environment, causing, among other things, distortions in the perception about the academic performance of students. The objective of this work is, thus, to propose a mechanism to execute periodic and non-intrusive authentications of users in VLEs.The proposed mechanism uses machine learning techniques to build recognition models based on the keystroke dynamics of users and it is also independent of the used VLE. A prototype of the proposed mechanism, integrated with Moodle, was implemented and applied to a postgraduate course with seventeen users. The recognition models generated by the prototype in the case study showed a performance above 92% of accuracy, which is a positive indication about the viability of the utilization of the proposed mechanism.

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“…FAR of above 98% has been reached for video based iris verification. Enhancing the performance of iris based authentication has gained the attention of a lot of researchers as reported in (Bowyer, Hollingsworth, & Flynn, 2013). Singh has suggested a noise removal method from the acquired iris images as well as authenticating only specific parts of the iris (Singh, 2014).…”

Section: ) Eye Based Featuresmentioning

confidence: 99%

Authentication systems: principles and threats

Abdulkader¹,

Atia²,

Mostafa³

2015

CIS

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Identity manipulation is considered a serious security issue that has been enlarged with the spread of automated systems that could be accessed either locally or remotely. Availability, integrity, and confidentiality represent the basic requirements that should be granted for successful authentication systems. Personality verification has taken multiple forms depending on different possession types. They are divided into knowledge based, token based, and biometric based authentication. The permanent ownership to the human being has increased the chances of deploying biometrics based authentication in highly secure systems. It includes capturing the biological traits, which are physiological or behavioral, extracting the important features and comparing them to the previously stored features that belong to the claimed user. Various kinds of attacks aim to take down the basic requirements at multiple points. This paper describes different types of authentication along with their vulnerable points and threatening attacks. Then it provides more details about the biometric system structure as well as examples of distinguishing biological characteristics, organized by their locations. It shows the performance results of various biometric systems along with the deployed algorithms for different components.

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A Survey of Iris Biometrics Research: 2008–2010

Cited by 94 publications

References 198 publications

Fusion of Iris Segmentation Results

Fusion of Iris Segmentation Results

Dinâmica da Digitação Aplicada à Autenticação Periódica de Usuários em Ambientes Virtuais de Aprendizagem

Authentication systems: principles and threats

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