The development of automated palmprint identification using major flexion creases

2.50
Hdl Handle:
http://hdl.handle.net/2436/241851
Title:
The development of automated palmprint identification using major flexion creases
Authors:
Cook, Thomas
Abstract:
Palmar flexion crease matching is a method for verifying or establishing identity. New methods of palmprint identification, that complement existing identification strategies, or reduce analysis and comparison times, will benefit palmprint identification communities worldwide. To this end, this thesis describes new methods of manual and automated palmar flexion crease identification, that can be used to identify palmar flexion creases in online palmprint images. In the first instance, a manual palmar flexion crease identification and matching method is described, which was used to compare palmar flexion creases from 100 palms, each modified 10 times to mimic some of the types of alterations that can be found in crime scene palmar marks. From these comparisons, using manual palmar flexion crease identification, results showed that when labelled within 10 pixels, or 3.5 mm, of the palmar flexion crease, a palmprint image can be identified with a 99.2% genuine acceptance rate and a 0% false acceptance rate. Furthermore, in the second instance, a new method of automated palmar flexion crease recognition, that can be used to identify palmar flexion creases in online palmprint images, is described. A modified internal image seams algorithm was used to extract the flexion creases, and a matching algorithm, based on kd-tree nearest neighbour searching, was used to calculate the similarity between them. Results showed that in 1000 palmprint images from 100 palms, when compared to manually identified palmar flexion creases, a 100% genuine acceptance rate was achieved with a 0.0045% false acceptance rate. Finally, to determine if automated palmar flexion crease recognition can be used as an effective method of palmprint identification, palmar flexion creases from two online palmprint image data sets, containing images from 100 palms and 386 palms respectively, were automatically extracted and compared. In the first data set, that is, for images from 100 palms, an equal error rate of 0.3% was achieved. In the second data set, that is, for images from 386 palms, an equal error rate of 0.415% was achieved.
Advisors:
Sutton, Raul Dr
Publisher:
University of Wolverhampton
Issue Date:
Jan-2012
URI:
http://hdl.handle.net/2436/241851
Type:
Thesis or dissertation
Language:
en
Description:
A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy
Appears in Collections:
E-Theses

Full metadata record

DC FieldValue Language
dc.contributor.advisorSutton, Raul Dren_GB
dc.contributor.authorCook, Thomasen_GB
dc.date.accessioned2012-09-07T13:06:24Z-
dc.date.available2012-09-07T13:06:24Z-
dc.date.issued2012-01-
dc.identifier.urihttp://hdl.handle.net/2436/241851-
dc.descriptionA thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophyen_GB
dc.description.abstractPalmar flexion crease matching is a method for verifying or establishing identity. New methods of palmprint identification, that complement existing identification strategies, or reduce analysis and comparison times, will benefit palmprint identification communities worldwide. To this end, this thesis describes new methods of manual and automated palmar flexion crease identification, that can be used to identify palmar flexion creases in online palmprint images. In the first instance, a manual palmar flexion crease identification and matching method is described, which was used to compare palmar flexion creases from 100 palms, each modified 10 times to mimic some of the types of alterations that can be found in crime scene palmar marks. From these comparisons, using manual palmar flexion crease identification, results showed that when labelled within 10 pixels, or 3.5 mm, of the palmar flexion crease, a palmprint image can be identified with a 99.2% genuine acceptance rate and a 0% false acceptance rate. Furthermore, in the second instance, a new method of automated palmar flexion crease recognition, that can be used to identify palmar flexion creases in online palmprint images, is described. A modified internal image seams algorithm was used to extract the flexion creases, and a matching algorithm, based on kd-tree nearest neighbour searching, was used to calculate the similarity between them. Results showed that in 1000 palmprint images from 100 palms, when compared to manually identified palmar flexion creases, a 100% genuine acceptance rate was achieved with a 0.0045% false acceptance rate. Finally, to determine if automated palmar flexion crease recognition can be used as an effective method of palmprint identification, palmar flexion creases from two online palmprint image data sets, containing images from 100 palms and 386 palms respectively, were automatically extracted and compared. In the first data set, that is, for images from 100 palms, an equal error rate of 0.3% was achieved. In the second data set, that is, for images from 386 palms, an equal error rate of 0.415% was achieved.en_GB
dc.language.isoenen
dc.publisherUniversity of Wolverhamptonen
dc.subjectpalmprinten_GB
dc.subjectpalm printen_GB
dc.subjectpalmaren_GB
dc.subjectflexion creasesen_GB
dc.subjectidenticationen_GB
dc.titleThe development of automated palmprint identification using major flexion creasesen_GB
dc.typeThesis or dissertationen
dc.type.qualificationnamePhDen
dc.type.qualificationlevelDoctoralen
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