Robust and Secure Iris Recognition

Pillai, Jaishanker K.; Patel, Vishal M.; Chellappa, Rama; Ratha, Nalini K.

doi:10.1007/978-1-4471-4402-1_10

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…The system in (Pillai, Patel, Chellappa, & Ratha, 2013) performs iris based identity confirmation using a database that includes images with errors either in the acquisition or preprocessing. It achieves a verification rate of 98.13%.…”

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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Section: ) Eye Based Featuresmentioning

confidence: 99%

Authentication systems: principles and threats

Abdulkader¹,

Atia²,

Mostafa³

2015

CIS

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Previous works about iris recognition stages

Harifi

Bastanfard

2015

2015 Forth International Conference on E-Technologies and Networks for Development (ICeND)

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An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture

Boada

Garcia

Ferreira

et al. 2017

I&U:EfD

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<p>Hyperspectral imaging entails data typically spanning hundreds of contiguous wavebands in a certain spectral range. Each spatial point in hyperspectral images is therefore represented by a vector whose entries correspond to the intensity on each spectral band. These images enable object and feature detection, classification, or identification based on their spectral characteristics. Novel architectures have been developed for the acquisition of compressive spectral images with just a few coded aperture focal plane array measurements. This work focuses on the development of a target detection approach in hyperspectral images directly from compressive measurements without first reconstructing the full data cube that represents the real image. Specifically, a sparsity-based target detection model that uses compressive measurement for the detection task is designed and tested using an optimization algorithm. Simulations show that it is possible to perform certain transformations to the dictionaries used in traditional target detection, in order to achieve an accurate image representation in the compressed subspace</p>

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Robust and Secure Iris Recognition

Cited by 3 publications

References 24 publications

Authentication systems: principles and threats

Authentication systems: principles and threats

Previous works about iris recognition stages

An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture

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