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dc.contributor.advisorCorcoran, Peter
dc.contributor.authorUngureanu, Adrian-Stefan
dc.date.accessioned2020-05-26T14:25:50Z
dc.date.available2020-05-26T14:25:50Z
dc.date.issued2019-11-27
dc.identifier.urihttp://hdl.handle.net/10379/15993
dc.description.abstractThis thesis investigates the suitability of unconstrained palmprint as a biometric modality for handheld devices equipped with a camera, such as smartphones. A detailed literature survey is provided, covering existing datasets, methods for region of interest extraction (ROI) extraction and feature extraction from palmprints. Following a series of exploratory experiments, a novel dataset of palmprints from 81 subjects and acquired using 5 different smartphone cameras is developed. Details are provided of initial data acquisitions, the final acquisition and management protocol and the associated Ethics Application. The dataset was collected in several acquisition phases over a period of 8 months. A set of baseline matching experiments is also detailed and manual mark-up of the palmprint data is included with the dataset. The accurate extraction of palmprint ROI was identified as a key component in the biometric recognition pipeline but the mark-up used for ROI extraction is not available in palmprint datasets. Thus, a second dataset was acquired using a 3D sensor and aligned camera with suitable mark-up data. Over 25,000 images were acquired from 26 subjects over the course of 1 year. Corresponding experiments were designed to evaluate a range of machine learning approaches to ROI extraction and a new quality measure was developed to compare the accuracy of ROI extraction for palmprints. Detailed experiments compared various ROI extraction techniques and have demonstrated that unconstrained palmprint can serve as a practical means of biometric authentication using standard smartphone cameras and without a need for specialized fingerprint or 3D face sensors.en_IE
dc.publisherNUI Galway
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectpalmprint recognitionen_IE
dc.subjectmachine learningen_IE
dc.subjectimage processingen_IE
dc.subjectbiometric recognitionen_IE
dc.subjectconsumer devicesen_IE
dc.subjectEngineering and Informaticsen_IE
dc.subjectElectrical and Electronic Engineeringen_IE
dc.titleContributions to unconstrained palmprint recognition on smartphonesen_IE
dc.typeThesisen
dc.contributor.funderScience Foundation Irelanden_IE
dc.local.noteBiometric recognition in unconstrained environments has received considerable attention recently, the palmprint being one of the characteristics with great potential. This thesis brings contributions to palmprint recognition in the context of smartphone use, with image databases, algorithms for image processing, and matching scenarios.en_IE
dc.local.finalYesen_IE
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland