A video-based hyper-focal imaging method for iris recognition in the visible spectrum

Tankasala, Sriram Pavan; Gottemukkula, Vikas; Saripalle, Sashi K.; Nalamati, Venkata Goutam; Derakhshani, Reza; Pasula, Raghunandan; Ross, Arun

doi:10.1109/ths.2012.6459852

Cited by 8 publications

(2 citation statements)

References 20 publications

(15 reference statements)

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“…A typical iris recognition system captures frontal images of the eye in the near-infrared (NIR) spectrum. However, recently, researchers have attempted to perform iris recognition in the visible spectrum, in non-cooperative scenarios with relaxed constraints [1][2][3][4][5][6][7][8] . Competitions such as NICE I and II, have encouraged researchers to develop effective algorithms for iris segmentation and feature extraction in the visible spectrum.…”

Section: Introductionmentioning

confidence: 99%

An indexing method for color iris images

Crihalmeanu

Ross

2015

SPIE Proceedings

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In this work, we study the possibility of indexing color iris images. In the proposed approach, a clustering scheme on a training set of iris images is used to determine cluster centroids that capture the variations in chromaticity of the iris texture. An input iris image is indexed by comparing its pixels against these centroids and determining the dominant clusters -i.e., those clusters to which the majority of its pixels are assigned to. The cluster indices serve as an index code for the input iris image and are used during the search process, when an input probe has to be compared with a gallery of irides. Experiments using multiple color spaces convey the efficacy of the scheme on good quality images, with hit rates closes to 100% being achieved at low penetration rates.

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Section: Introductionmentioning

confidence: 99%

An indexing method for color iris images

Crihalmeanu

Ross

2015

SPIE Proceedings

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“…Recent improvements in iris image acquisition include the usage of wavefront coding [52,53], and hyper-spectral imaging systems [54].…”

Section: Introduction 11mentioning

confidence: 99%

Techniques for Ocular Biometric Recognition Under Non-ideal Conditions

Ross

Jillela

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This thesis would not have been possible without the support and guidance of many people. First, I would like to express my deepest gratitude to my advisor, Dr. Arun Ross. I vividly remember my initial interactions with him during the first semester of my Masters, when he taught the Introduction to Biometrics course. Little did I know that he would greatly inspire me in pursuing both, Masters and PhD in the field of biometrics. His dedication, patience, and hardworking nature have constantly inspired me over the years. Working with him has taught me to conduct research with rigor, and present my work with clarity. I truly consider it as a privilege to have him as an advisor and instructor throughout my graduate studies. I would like to thank my committee members for their valuable guidance and suggestions in my research. Dr. Xin Li's courses in image-and video-processing have been extremely helpful in getting started with my research. My thesis has been greatly benefitted by Dr. Donald Adjeroh's detailed feedback. It was Dr. Lawrence Hornak who inspired me to think practically in research. Special thanks to Dr. Vijayakumar Bhagavatula, who agreed to be a member of my committee on a short notice. His discussions during IARPA meetings and subsequent thesis presentations reminded me to maintain novelty in my work.

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Iris Recognition Using Color and Texture Features

Pavaloi

Ignat

2017

Soft Computing Applications

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A video-based hyper-focal imaging method for iris recognition in the visible spectrum

Cited by 8 publications

References 20 publications

An indexing method for color iris images

An indexing method for color iris images

Techniques for Ocular Biometric Recognition Under Non-ideal Conditions

Iris Recognition Using Color and Texture Features

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