2018 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)

Peter Corcoran

2018 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)

Electrical & Electronic Engineering

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

In this paper the increasing use of biometric authentication on handheld devices is considered and the importance of accurate iris segmentation in implementing an embedded authentication workflow based on iris authentication is explained. A deep learning scheme is then developed and an appropriate augmentation method is presented to solve the problem of the iris segmentation task in handheld devices. Initial comparisons with publicly available iris segmentation algorithms show significant performance improvements, particularly on challenging image datasets designed to mimic the image quality obtained from a handheld device.

Original language	English (Ireland)
Title of host publication	Enhancing Iris Authentication on Handheld Devices Using Deep Learning Derived Segmentation Techniques
Publication status	Published - 1 Jan 2018

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Bazrafkan, S,Corcoran, P,Mohanty, SP,Corcoran, P,Li, H,Sengupta, A,Lee, JH

Cite this

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title = "2018 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)",

abstract = "In this paper the increasing use of biometric authentication on handheld devices is considered and the importance of accurate iris segmentation in implementing an embedded authentication workflow based on iris authentication is explained. A deep learning scheme is then developed and an appropriate augmentation method is presented to solve the problem of the iris segmentation task in handheld devices. Initial comparisons with publicly available iris segmentation algorithms show significant performance improvements, particularly on challenging image datasets designed to mimic the image quality obtained from a handheld device.",

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AB - In this paper the increasing use of biometric authentication on handheld devices is considered and the importance of accurate iris segmentation in implementing an embedded authentication workflow based on iris authentication is explained. A deep learning scheme is then developed and an appropriate augmentation method is presented to solve the problem of the iris segmentation task in handheld devices. Initial comparisons with publicly available iris segmentation algorithms show significant performance improvements, particularly on challenging image datasets designed to mimic the image quality obtained from a handheld device.

M3 - Conference Publication

BT - Enhancing Iris Authentication on Handheld Devices Using Deep Learning Derived Segmentation Techniques

ER -

2018 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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Cite this