Loading...

Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform

©2017 Textbook 47 Pages

Summary

Biometrics refers to the authentication techniques that depend on measurable physical characteristics and behavioural characteristics to identify an individual. The biometric systems consist of different stages such as image acquisition, preprocessing, feature extraction and matching. Biometric techniques are widely used in the security world. The various types of biometric systems use different techniques for the preprocessing, feature extraction and classifiers.The dorsum of the hand is known as the finger back surface. It is highly used for personal authentication and has not yet attracted the attention of convenient researchers. It is mostly used due to contact free image acquisition. It is reported that the skin pattern on the finger-knuckle is extremely rich in texture due to skin folds and creases, and hence, can be considered as a biometric identifier. Furthermore, advantages of using Finger Knuckle Print (FKP) include rich in texture features, easily accessible, contact-less image acquisition, invariant to emotions and other behavioral aspects such as tiredness, stable features and acceptability in the society. As a result of that, there is less known use of finger knuckle pattern in commercial or civilian applications.
The local features of an enhanced palmprint image are extracted using Fast Discrete Orthonormal Stockwell Transform (FDOST). The Fourier transform of an image is obtained by increasing the scale of FDOST to infinity. The Fourier transform coefficients extracted from the palmprint image and FKP image are considered as the global information. The local and global information are physically linked by means of the framework of time frequency analysis. The global feature is exploited to refine the arrangement of FKP images in matching. The proposed schemes make use of the local and global features to verify finger knuckle-print images. The weighted average of the local and global matching distances is taken as the final matching distance of two FKP images. The investigational results indicate that the proposed works outperform the existing works.

Excerpt

Table Of Contents


Kumar, N.B. Mahesh, Premalatha, K.: Finger Knuckle-Print Authentication Using Fast
Discrete Orthonormal Stockwell Transform, Hamburg, Anchor Academic Publishing
2017
PDF-eBook-ISBN: 978-3-96067-703-1
Druck/Herstellung: Anchor Academic Publishing, Hamburg, 2017
Bibliografische Information der Deutschen Nationalbibliothek:
Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen
Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über
http://dnb.d-nb.de abrufbar.
Bibliographical Information of the German National Library:
The German National Library lists this publication in the German National Bibliography.
Detailed bibliographic data can be found at: http://dnb.d-nb.de
All rights reserved. This publication may not be reproduced, stored in a retrieval system
or transmitted, in any form or by any means, electronic, mechanical, photocopying,
recording or otherwise, without the prior permission of the publishers.
Das Werk einschließlich aller seiner Teile ist urheberrechtlich geschützt. Jede Verwertung
außerhalb der Grenzen des Urheberrechtsgesetzes ist ohne Zustimmung des Verlages
unzulässig und strafbar. Dies gilt insbesondere für Vervielfältigungen, Übersetzungen,
Mikroverfilmungen und die Einspeicherung und Bearbeitung in elektronischen Systemen.
Die Wiedergabe von Gebrauchsnamen, Handelsnamen, Warenbezeichnungen usw. in
diesem Werk berechtigt auch ohne besondere Kennzeichnung nicht zu der Annahme,
dass solche Namen im Sinne der Warenzeichen- und Markenschutz-Gesetzgebung als frei
zu betrachten wären und daher von jedermann benutzt werden dürften.
Die Informationen in diesem Werk wurden mit Sorgfalt erarbeitet. Dennoch können
Fehler nicht vollständig ausgeschlossen werden und die Diplomica Verlag GmbH, die
Autoren oder Übersetzer übernehmen keine juristische Verantwortung oder irgendeine
Haftung für evtl. verbliebene fehlerhafte Angaben und deren Folgen.
Alle Rechte vorbehalten
© Anchor Academic Publishing, Imprint der Diplomica Verlag GmbH
Hermannstal 119k, 22119 Hamburg
http://www.diplomica-verlag.de, Hamburg 2017
Printed in Germany

TABLE OF CONTENTS
CHAPTER NO.
TITLE
PAGE NO
.
1
INTRODUCTION TO BIOMETRICS
5
1.1 INTRODUCTION
5
1.1.1 Biometric Systems
6
1.2 PALMPRINT BIOMETRICS
9
1.2.1 Preprocessing and ROI Extraction for
Palmprint Biometrics
10
1.3 FINGER KNUCKLE- PRINT BIOMETRICS 12
1.3.1 Finger Knuckle-Print Anatomy
12
1.3.2 Preprocessing and ROI Extraction for
Finger Knuckle-Print Biometrics
14
1.4 PROS OF FINGER KNUCKLE-PRINT
AND PALMPRINT
15
1.5 LOCAL AND GLOBAL FEATURES
16
1.6 PROBLEM STATEMENT
17
1.7 MOTIVATION
17
1.8 OBJECTIVES
18
1.9 BIOMETRIC DATASETS
18
1.9.1 College of Engineering ­ Pune (COEP)
Palmprint Datasets
18
1.9.2 The PolyU Palmprint Datasets
19
1.9.3 Indian Institute of Technology
(IIT Delhi) Touchless Palmprint
Datasets
19
1.9.4 The PolyU Finger Knuckle-print
Datasets
19

1.10 PERFORMANCE METRICS
20
1.10.1 False Acceptance Rate and False
Rejection Rate
21
1.10.2 Speed
23
1.10.3 Equal Error Rate (EER)
23
1.10.4 Correct Classification Rate (CCR)
23
1.10.5 Data Presentation Curves
23
1.10.5.1 Receiver Operating
Characteristic (ROC) Curve
24
2
FINGER KNUCKLE-PRINT IDENTIFICATION
BASED ON LOCAL AND GLOBAL FEATURE
EXTRACTION USING FAST DISCRETE
ORTHONORMAL STOCKWELL TRANSFORM 25
2.1 OVERVIEW OF FAST DISCRETE
ORTHONORMAL STOCKWELL
TRANSFORM
25
2.2 LOCAL ­ GLOBAL FEATURE EXTRACTION
AND MATCHING
25
2.2.1 Local Feature
25
2.2.2 Global Feature
26
2.3 LOCAL GLOBAL INFORMATION FUSION
FOR KNUCKLE-PRINT RECOGNITION
31
2.4 EXPERIMENTAL RESULTS AND
DISCUSSION
31
2.5 SUMMARY
37

3
CONCLUSIONS AND FUTURE WORK
38
3.1 SUMMARY AND CONCLUSIONS
38
3.2 FUTURE WORKS
39
REFERENCES
41


5
CHAPTER 1
INTRODUCTION TO BIOMETRICS
This chapter emphasize the significance of palmprint biometrics, Finger
knuckle-print biometrics and their performance measures. Also the characteristics of
local and global features are presented in this chapter.
1.1
Introduction
Biometrics refers to technologies for measuring and analyzing a person's
physiological or behavioural characteristics (Wayman 2001). These characteristics
are unique to individuals and it can be used to verify or identify a person. The
applications of biometrics are considerably increased in the last years and it is
expected in the near future. Depending on the deployment of biometrics, the
applications are categorized in the following five main groups: forensic, government,
commercial, health-care and travelling-immigration. However, some applications are
common to these groups such as physical access, personal computer/network access,
time and attendance, etc.
Biometrics has increasing attention in the e-world. Different types of
biometrics were used in different applications. There are very few best biometric
systems available in the market. The three different types of authentication are used
in security system. The first type of authentication is password system and Postal
Index Number (PIN) system. The second type of authentication is a card key, smart
card or token system. The third type of authentication is a biometric technology. Out
of these types of authentications in security system, biometric is the best secure and
expedient authentication tool.
Biometrics cannot be easily borrowed, stolen or forgotten compared to the
traditional security systems. The forgery of the biometric system is practically
impossible. It refers to the person's unique physical or behavioural characteristics to

6
distinguish or authenticate their own identity. The various physical biometrics are
fingerprints (Belguechi et al 2013) hand or palm geometry (Matos et al 2012), retina
(Hussain et al 2013), iris technique considers as a resemblance measure in certain
biometrics systems (Miyazawa et al 2008), face (Yuchun et al 2002), palmprint (Sun
et al 2005) hand vein (Huang et al 2013), palm vein (Venkat Narayana & Preethi
2010), finger knuckle-print (Nanni & Lumini 2009) or ear (Middendorff 2011). The
behavioural biometrics is signature (Bertolini et al 2010), voice (Hollein 2002),
keystroke pattern (Pin et al 2013) and gait (Hoang et al 2013).
1.1.1 Biometric Systems
The biometric trait can be acquired from an individual and then the feature
set is extracted from the acquired data. Finally, this feature set is compared with the
template set in the database. Therefore biometric system is also referred as a pattern
recognition system. Biometric system may operate either in verification mode or
identification mode based on the application it is used in the security system. In the
verification mode, an individual's identity is authenticated in the security system by
comparing the captured biometric trait with the own biometric template(s) stored in
the system database. An individual may recognize one's identity with the help of
PIN, a user name, or a smart card. Here the biometric system performs a one to one
matching to determine whether person's individuality is correct or not. Identity
verification is mainly used for positive recognition. The objective of the individuality
verification is to avert several persons from consuming the similar uniqueness. The
system recognizes an individual by searching in the verification templates of all the
users in the database for a match in the identification approach. Therefore, the system
performs a one-to-many matching to establish an individual's identity (or fails if the
subject is not enrolled in the system database) without the subject having to claim an
identity.

7
Figure 1.1 Working principle of biometric system (Simon & Mark
2001)
The various steps involved in Figure 1.1 (Simon & Mark 2001) are given below:
Step 1: Capture the chosen biometric;
Step 2: Process the biometric and extract and enroll the biometric templates;
Step 3: Store the template in a local repository, or a portable token such as a smart
code;
Step 4: Live scan the chosen biometric;
Step 5: Process the biometric and extract the biometric template;
Step 6: Match the scanned biometric template against stored templates;
Step 7: Provide a matching score to business application;
Step 8: Record a secure audit trail with respect to system use.
The biometric system is divided into four main modules.
1.
Sensor Module: It captures the biometric trait of a person.
2.
Feature Extraction Module: The biometric trait obtained from the
sensor module is processed to extract a set of or salient or
discriminatory features.
People
Biometric
Devices
Biometric
Enrolment
Template
Storage
Biometric
Devices
Biometric
verification
Template
Storage
Business
Applications
1
2
3
4
5
8
6
7

8
3.
Pattern Matching Module: The information mined in the feature
extraction module is matched with the templates to generate the
matching scores.
4.
System database module: It is used in the biometric system to store
the biometric templates of the enrolled users. The enrolment module
is responsible for enrolling individuals in the biometric system
database. The biometric reader is used to scan the biometric traits of
an individual to produce the digital representation or feature values of
the biometric characteristics during the enrolment phase. The data
captured during the enrolment process may or may not be supervised
by human depending on the application. An eminence testing is
usually achieved to ensure that the acquired sample is relatively
processed by successive stages. The feature extractor is used to
process the digital representation for facilitate the matching to
generate compact but expensive representation is called as template.
The template is stored in the central database of a biometric system
depending on the application. The templates are also recorded on a
smart card issued to the individual. Usually, different templates of an
individual are stored to account for variations observed in the
biometric trait and the templates in the database may be updated over
time.
The two different techniques to measure the biometric accuracy are the
False Acceptance Rate (FAR) and False Rejection Rate (FRR). The limited entry is
allowed to authorize the users by two methods focussed on the system's ability. The
sensitivity of the mechanism is adjusted whatever matches to the biometrics. Based
on that sensitivity, the biometric measures can vary significantly.

9
1.2
Palmprint Biometrics
Palmprint verification is implemented in different way compared to the
fingerprint technology. The optical readers used in fingerprint technology are used in
palmprint scanning. The size of the palmprint scanner is bigger. It has a limiting
factor when used in workstations or mobile devices. The palms of the human hands
contain pattern of ridges and valleys much like the fingerprints. The region of the
palm is greatly higher than the region of a finger. Therefore palmprints are more
distinctive than the fingerprints. The palmprint scanner is used to capture the large
area of the palm. The low resolution scanner is used to capture the additional
distinctive features such as principal lines and wrinkles in the palmprint. It is very
cheap. Finally, it is used to capture all the features of the palmprint such as hand
geometry, ridge and valley features (e.g., minutiae and singular points such as deltas),
principal lines, and wrinkles.
Palmprint recognition inherently implements many of the same matching
characteristics that have allowed fingerprint recognition to be one of the most well-
known and best publicized biometrics. Both palm and finger biometrics is
represented by the information presented in a friction ridge imprint. The palms and
fingerprints are used as a trusted form of identification for more than a century. The
image captured from the palm region of the hand refers to the palmprint. The image
captured from a scanner or Charge Coupled Device (CCD) is known as online image.
The image taken with the help of ink and paper are known as offline image. The palm
itself consists of principal lines,
wrinkles
(secondary lines) and
epidermal ridges
. The
palmprint features are different from fingerprint features. The palmprint also
contains other features such as indents and marks. These features are used to compare
one palm with another palm. Palmprints are used for illegal, pathological, or
profitable applications.
The palmprint pattern cannot duplicate with the other people, even in
monozygotic twins. Hence palmprint is used as high reliable human identifier. The
details of the palmprint ridges are stable. The information remains unchanged from

Details

Pages
Type of Edition
Erstausgabe
Year
2017
ISBN (PDF)
9783960677031
ISBN (Softcover)
9783960672036
File size
6.1 MB
Language
English
Institution / College
Bannari Amman Institute of Technology
Publication date
2017 (November)
Grade
7.0
Keywords
Biometrics Dorsum Security Fourier transform Time frequency analysis Image acquisition Biometric recognition Global security Personal authentication Biometric system Authentication technique
Previous

Title: Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform
book preview page numper 1
book preview page numper 2
book preview page numper 3
book preview page numper 4
book preview page numper 5
book preview page numper 6
book preview page numper 7
book preview page numper 8
book preview page numper 9
book preview page numper 10
book preview page numper 11
47 pages
Cookie-Einstellungen