Contributions to unconstrained palmprint recognition on smartphones

This 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.

Funder

Science Foundation Ireland

Publisher

NUI Galway

Rights

Attribution-NonCommercial-NoDerivs 3.0 Ireland

cbn

Collections

University of Galway Theses (PhD Theses)