ENHANCING ASSISTIVE TECHNOLOGIES:

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ENHANCING ASSISTIVE TECHNOLOGIES:

THROUGH THE THEORETICAL ADAPTATION OF

BIOMETRIC TECHNOLOGIES TO PEOPLE OF VARIABLE ABILITIES



A Dissertation

Presented to the

Faculty of the

School of Business

Kennedy
-
Western University


In Partial Fulfillment

of the
Requirements for the Degree of

Doctor of Philosophy in

Management Information Systems



by

William J. Lawson, Ph.D.

Tampa, Florida


i

















© 2003


William J. Lawson


All Rights Reserved











ii



Dedication




This dissertation is dedicated
to my Grandmother, Charity Lawson who
passed away September 14, 2000. Following a short illness her life was
taken away unexpectedly. After our biological mother left when I was 2
years old and my brother Tim was only 3 months old Grandma became the
mother

to Tim and I.


My Grandma had been a constant source of inspiration and
encouragement in my life. Grandma was born August 25, 1918 near
Chicago, Illinois to Romanian Immigrates (Gypsies). She spent most of her
childhood traveling with her parents as a

fortuneteller in a circus.


If you asked me to tell you what about my Grandmother stood out, I
would have to say that she was an extremely proud woman. She held her
head high and kept great faith through the many trials and tribulations
throughout her
life. I would also tell you that I remember her explaining the
“Golden Rule” to me when I was 6 years old and I will never forget, she said
that it meant to “Do onto others as you wish done onto you”. I am so very
proud of my Grandmother
-

a woman endued w
ith courage, strength, and
the will to fight. I am fortunate to have not only loved her, but to have been
loved by her. What a gift she gave me, for as I write this dedication a tear
rolls down my cheek.

iii




I love and miss you Grandma…

Acknowledgements





Without question my family have felt the pain and joy of this project,
and I thank them for their love, support and endurance of many unique
hardships.


This project could not have happened without the enthusiasm and
guidance of so many others. It
would be impossible to list the names of all
of those that have encouraged me in the adaptation of biometrics as an
assistive technology. I would therefore, like to pay homage to the
insightfulness and courage of the one individual whom opened my eyes to
t
he assistive possibilities of biometric. That person is Michael Burks,
Public Relations Officer of the International Center for Disability Research
on the Internet. Thank you, Michael...


Finally, a thank you to AT&T (my financial sponsor) for their de
cision to
support this project came from their individual leadership. They are
leaders whom recognize the value and importance of this study to
business and society.



iv



v



Table of Contents


Page

List of Tables

................................
................................
..........................

xiii

List of Figures

................................
................................
........................

xiv

List of Images

................................
................................
.........................

xv

List
of Charts

................................
................................
..........................

xvii

Abstract of Dissertation

................................
................................
........

xviii

Chapter 1. Introduction

................................
................................
......

1
-
35

Proclamation of Problem

................................
................................
.

1

Foundation of the Study

................................
................................
..

5

Significance of the Study

................................
................................
.

6

Scope of the Study

................................
................................
..........

7

Rational of the Study

................................
................................
.......

7

Glossary of Terms

................................
................................
...........

8

Overview of the Study

................................
................................
.....

34

Chapter 2.
Review of Related Literature

................................
.........

36
-
52

Mainstream Biometric Technologies

................................
...............

37

Emerging Biometric Technologies

................................
..................

37

Radio Frequency Identification (RFID)


................................
...........

39

Smart Card Technologies

................................
................................

40

Page

vi



Assistive Technologies

................................
................................
....

42

Cultural Barrier (Disabled & Elderly)

................................
...............

43

Univers
al Design

................................
................................
.............

45

Adaptation to People of Variable Abilities

................................
.......

45

Privacy/Legal Issues

................................
................................
........

46

Security Issues

................................
................................
................

47

Disability Demographics

................................
................................
..

49

Electronic News Sources

................................
................................

50

Study Associated Standards

................................
...........................

50

Summarization of Related Literature

................................
...............

51

Chapter 3. Applied Research Met
hodologies
................................
.

53
-
68

Data Gathering Methods

................................
................................
.

54

Historical Documentation

................................
................................

55

Quantitative Research Tools

................................
...........................

56

Web
-
Based Surveys

................................
................................
..

57

One
-
on
-
One Interviews

................................
..............................

58

Qualitative Research Tools

................................
.............................

60

Symposiums

................................
................................
.....

60

Teleconferences

................................
................................
.....

63

Page

Technical Committees

................................
...............................

63

vii



Elec
tronic Mail Exchanges

................................
.........................

64

Communication Participants

................................
......................

65

Database of Study

................................
................................
...........

65

Accuracy, Reliability, Validity of Data

................................
..............

65

Originality and Limitation of Data

................................
....................

67

Methodological Summary

................................
................................

67

Chapter 4. Analysis of Data

................................
...........................

69
-
186

What is a Biometric

................................
................................
.........

70

Contrasting Aut
hentication Methods

................................
........

71

Contact Biometric Technologies

................................
.....................

73

Fingerprint Identification

................................
............................

74

Palm Print and Footprint Identification

................................
.....

77

Hand Geometry

................................
................................
......

79

Dynamic Keystroke Authentication

................................
...........

80

Dynamic Signature Recognition

................................
...............

82

Contactless Biometric Technologies

................................
...............

83

Facial Geometry

................................
................................
......

84

Facial Thermography

................................
................................

85

Page

Iris Scan Recognition

................................
................................

86

Retina Scan Recognition

................................
..........................

89

viii



Voiceprint Verification

................................
...............................

90

Accuracy

................................
................................
..........................

91

Liveness Test

................................
................................
..................

92

Advantages

................................
................................
......................

93

Disadvantages

................................
................................
.................

94

Existing Standards

................................
................................
...........

95

Emerging Biometric Technologies

................................
..................

96

Brainwave
Biometric
................................
................................
..

97

DNA Identification
................................
................................
......

98

Vascular Pattern Recognition

................................
...................

99

Body Odor Recognition

................................
.............................

102

Fingernail Bed Recognition

................................
.......................

103

Gait Recognition

................................
................................
......

103

Handgrip Recognition

................................
..............................

104

Ear Pattern Recognition

................................
............................

105

Body Salinity Identification

................................
........................

106

Infra
-
Red Fingertip I
maging & Pattern Recognition

.................

107

Page

Storage Methodologies

................................
................................
...

108

Client
-
Server Architecture

................................
.........................

109

Distributed Architecture

................................
.............................

109

ix



Radio Frequency Identification (RFID)

................................
......

110

Smart Card Technologies

................................
.........................

111

Hybrid Architecture

................................
................................
....

114

Existing Standards

................................
................................
.....

115

Disability Stat
istics

................................
................................
...........

118

Privacy/Legal Issues

................................
................................
........

121

Civil Rights


................................
................................
......

122

Individual Anonymity

................................
................................
.

123

Biometric Technologies

................................
.............................

124

Storage Methodologies

................................
..............................

125

Private Institutions

................................
................................
.....

127

Government Facilities

................................
...............................

128

Public Places

................................
................................
......

128

Misuse of Personal Data

................................
...........................

129

Profiling (Big

Brother Watching)

................................
...............

136


Page

Security Issues

................................
................................
................

138

Biometrics Technologies

................................
...........................

139

Storage Methodologies

................................
.............................

139

Assistive Technologies

................................
..............................

142

x



Existing Standards

................................
................................
....

142

Cultural Barriers/Perceptions

................................
..........................

145

The Elderly (Aging) Paradigm

................................
...................

146

Old Disability Paradigm

................................
.............................

146

New Disa
bility Paradigm

................................
...........................

147

Ability Sequestration of
Society

................................
.................

157

Biometrics Technologies

................................
...........................

158

Biometric Technology Markets

................................
........................

159

Law Enforcement

................................
................................
.....

160

Government Sector

................................
................................
...

161

Travel and Immigration

................................
..............................

162

Corporate Sector

................................
................................
......

164

Financial Sector

166

Healthcare Sector

................................
................................
......

167


Page

Adaptation to People of Variable Abilities

................................
.......

168

Reasonable Accommodation

................................
....................

168

Smart Card Interface

................................
................................
.

169

Control

................................
................................
......

171

Universal Design


................................
................................
......

171

xi



Fused Biometric Solution

................................
..........................

176

Exoskeleton

................................
................................
......

179

Implementation Strategies

................................
...............................

181

Risk Assessment

Methodology (RAM)


................................
....

183

Integration Concerns

................................
................................
.

184

Enrollment/Administration Practices

................................
..........

185

Training/Education

................................
................................
....

185

Alternative Authentication Methods

................................
..........

186

Auditing

................................
................................
.....

187

Accountability

................................
................................
......

187

Oversight

................................
................................
......

187

Chapter 5. Summary, R
ecommendations and Conclusions

.....

188
-
xxx

Mainstream Biometric Technology Summary

................................
.

188

Emerging Biometric Technology Summary

................................
.....

189

Page

Summary of Cultural Barriers

................................
..........................

189

Assistive Technology Summary

................................
......................

193

Universal Design Summary

................................
.............................

195

Recommendations for Universal S
tandard

................................
.....

195

Recommendations for Adaptation of Biometrics

.............................

196

Recommendations for Storage Methodologies

...............................

197

xii



Recommendations for Fused Biometric Solutions

..........................

198

Conclusions

................................
................................
.....................

201

References
................................
................................
........................

203
-
214

Appendices

Appendix 1: To Be Or Not To Be? (Survey Introduction)

................

A
-
1

Appendix 2: Online Survey: Use of Biometrics and Neural
Implants

................................
................................
........

A
-
2

Appendix 3: One
-
on
-
One Interview Questionnaire

.........................

A
-
3

Appendix 4: Final Result Matrix: Online Survey
-

Per
Question Breakdown

................................
....................

A
-
4

Appendix 5: Fused Result: Online Survey


By Agreement
Levels

................................
................................
...........

A
-
5

Appendix 6: Aggregated Results of One
-
on
-
One Interview
Questions

................................
................................
.....

A
-
6













xiii





























List of Tables

Page

Table 1: Twelve Known One
-
on
-
One Interview Participants

.................

57

Table 2: List of Teleconference Sponsoring Organizations

..................

63

Table 3: Standard Biometric Header Followed by the BDB and


the SB

................................
................................
.......................

177

xiv



List of Figures


Page

Figure 1: Graphical Representation of Employed Research


Approach

................................
................................
..................

54

Figure 2: Structure of CBEFF Data Block

................................
..............

177















xv



List of Images


Page

Image 1: D
epiction of Fingerprint Patterns and Minutiae

...................

75

Image 2: Comparison of an Ultrasonic and Optical Scanned


Fingerprint

Image

................................
................................
..

77

Image 3: Depiction of Palm Print Patterns and Minutiae

......................

78

Image 4: Depiction of Hand Geometry Recognition Process

.............

80

Image 5: Ex
ample of the Dynamic Keystroke Authentication


Process

................................
................................
..................

81

Image 6: Depiction of Dynamic Signature

................................
...........

83

Image 7: Depiction of Facial Geometry Biometric

..............................

85

Image 8: Depiction of Facial Thermography Pattern Biometric

.........

86

Image 9: Depiction of Iris Scan Biometric

................................
...........

87

Image 10: Left eye of researcher (Dr. William Lawson)

.....................

88

Image 11: Depiction of Retina Scan Biometric

................................
...

90

Image 12: Depiction of Voiceprint Verification Biometric

...................

91

Image 13: Depiction of EEG Brain waveforms

................................
...

98

Image 14: Delineation of Vascular Scan Pa
ttern

................................

100

Image 15: Before and After Pictures of Spider Vein Procedure

........

101

Image 16: Before and After Pictures of Varicose Vein Procedure

.....

101

xvi



List of Images (continued)


Page

Image 17: Magnification of Human Nail Bed

................................
......

103

Image 18: Identification of Measura
ble Ear Features
.........................

106

Image 19: Rendering of Fingertip Thermo Mapping Technique

........

108

Image 20: Smallest RFID Chip

................................
............................

111

Image 21: Component Parts of Contactless Smart Card

...................

112

Image 22: Flow of Smart Card Reader/Writer Functions

...................

113

Image 23: Inductive Couplin
g for Contactless Smart Card

................

114

Image 24: Example of a Biometric Identification Smart Card

............

141

Image 25: INSPASS Station

................................
................................

162

Image 26: Rendering of a Exoskeleton

................................
...............

179

Image 27: Example of Neural Interface

................................
..............

180








xvii



List of Charts


Page

Chart 1: American D
isability Statistics, 1999

................................
......

120

Chart 2: Canadian Disability Statistics, 1998

................................
......

120

Chart 3: European Disability Statistics, 2001

................................
......

121

Chart 4: Potential Abuses of Power

................................
.................

133
-
136

Chart 5: Fused Biometric Solution Decision Flow Chart

....................

200


xviii



Abstract of Dissertati
on


ENHANCING ASSISTIVE TECHNOLOGIES:

THROUGH THE THEORETICAL ADAPTATION OF

BIOMETRIC TECHNOLOGIES TO PEOPLE OF VARIABLE ABILITIES


by

William J. Lawson, Ph.D.

Tampa, Florida


THE PROBLEM


Within the international culture of today’s information age t
here exist(s)
barriers to the adaptation of a secure access methodologies to electronic
devices and technology for people of variable abilities. This problem to be
addressed is that of a threefold design, each element is interconnected
and of an iterative
nature.


The first element of the threefold problem is the lack of an international
assistive technology interface standard(s) that are based on universal
design philosophies, the second element is the cultural barriers that have
been created by the m
indset of the international society, and the final
(third) resulting element is that the first two have created a shortage of
qualified personnel in the workplace.

xix




There also exists a theoretical assistive technology resolution that
could feasibly b
e adapted to the environments of schools, businesses,
and the international society at large. Biometric technologies could be
fused with other technologies both existing and emerging to play a
significant role in the eradication of the threefold problem.

T
HE METHOD


While the basal premise of this dissertation is that of original
innovation. There is no denying that the supporting elements of the
references have lent themselves to this paper are fundamentally based on
the eternal philosophies of applie
d research. It is the first
-
hand accounts
and experiences of those whom have come before that has lead to the
transition of emerging theories and technologies to origin of what is now
known as historical documentation. It is the historical documentation th
at
will add credence to the premise and this dissertation.


The exploration of case studies and technology trails was invaluable in
the research process. The exploration has allowed for the formation of
new case based approaches to address the validit
y and redundancy of
the research. The descriptive online surveys, one
-
on
-
one interviews,

conferences, teleconferences, and committees brought
into play the
cultural psyche and philosophies of the international communities.

xx




The quantification and quali
fication of the research is based on the
existence of the encompassed commonalities between all of the acquired
data and research methods. The margin for error is subjective in nature
and left to the item
-
by
-
item interpretation of each individual person.

T
HE FINDINGS


The absolute majority of the research material, findings, and available
technologies predominately tend to support the feasible adaptation of
biometrics to people of variable ability levels. Currently, with respect to the
threefold problem

the findings demonstrate that element one and three
can be eradicated today. However, element two, the shift of the cultural
barrier (paradigm) cannot be accomplished until elements one and three
have been put into effect. Once element one and three have
been
successfully put into effect, it will take several years or maybe a decade
for element two of the threefold problem to be eradicated or at the very
least significantly transformed.


1



Introduction

Chapter 1



The information age has already revolu
tionized the way in which we
live our lives from day to day. Each and everyday, a multitude of labor
-
intensive tasks are automated via some type of electronic device or
software application. The aforementioned growth of electronics and
technology has resul
ted in a greater demand for a rapid and defined
technique on how to adapt and implement emerging technologies to the
ever
-
changing environment of today. However, businesses and the
international society must not neglect to remember that with every
advance
of automation of technology comes the need to invent a
standardized interface in order to properly facilitate the need for individual
access and control.

Proclamation of Problem


Within the international culture of today’s information age there exist
b
arriers to the adaptation of a secure access methodology to electronic
devices and technology for people of variable abilities. This problem to be
addressed is that of a threefold design, each element is interconnected
and of an iterative nature.


2




The

first element of the threefold problem is the lack of an international
assistive technology interface standard(s) that are based on universal
design philosophies, the second element is the cultural barriers that have
been created by the mindset of the int
ernational society, and the final
(third) resulting element is that the first two has created a shortage of
qualified personnel in the workplace.


There also exist a theoretical assistive technology resolution that could
feasibly be adapted to the en
vironments of schools, businesses, and the
international society at large. Biometric technologies could be fused with
other technologies both existing and emerging to play a significant role in
the eradication of the threefold problem.


The critical sh
ortage of qualified personnel in the workplace is partly
related to the change of societies from that of an industrial based
workforce to a knowledge based workforce, partly because the baby
boomers have only had about half as many children as their parent
s, and
partly due to medical advances (Schaie & Schooler, 1998). As a result the
number of 20 to 24 year olds entering the workforce continues to fall
(NCD, 2001).


This critical shortage has forced employers to rethink their recruitment
strategies and

look towards targeting chronological mature

people, and
people with disabilities (variable abilities) (NCD, 2001). It is important to
3



recognize that people with disabilities are the largest minority group, they
cross all ethnic, racial, gender, chronologi
cal groups, and number at
around 54 million Americans and growing (U.S. Department of Labor
[USDOL], 2002). Out of the 29 million working age adults with variable
abilities in the U.S., about two thirds are unemployed and nearly 80
percent of that two thir
ds would like to work but have not had the
opportunity to do so (USDOL, 2002).


While people with variable abilities may have the desire to work, they
still may have to overcome the formidable attributes of the cultural barrier
or innate characteristi
cs of a disenabling mental, physical, or emotional
barrier. Cultural barriers embody numerous complex, dynamic, and
diverse challenges to be overcome. These challenges are related to but
are not limited to organizational, management, and worker cultures. I
n
plain terms, it is discrimination (Hagner & DiLeo, 1993). To overcome the
disenabling effects of mental, physical, or emotional barriers, society at
large has looked towards the properties rewards of assistive technologies
for reinforcements.


Assis
tive technologies persists to grow at a break neck pace, society
has not evolved rapidly enough to maintain pace with the necessities of a
universally conceived access and control solution. With respect to the
threefold problem, the adaptation or fusion of

biometric technologies and
4



smart card technologies to facilitate access and control is one technique
that can be employed to accomplish such a daunting chore.


Even in the technologically advanced environment of today, the
derivational technologies of

biometrics are still considered to be in the
category of emerging technologies. Typically, an emerging technology
inhabits what is referred to as the development stage and is thereby
fundamentally proprietary in nature. Therefore, national or internationa
l
adaptation and implementation standards are traditionally not established
until it is financially worthwhile to do so or until a profound episode
demonstrates the necessity for a particular technological solution.


The necessity for a particular techn
ology is typically directly related to
the desires of the human psyche (élan vital). Factors surrounding those
desires may possibly be demonstrated in the form of protection (such as
self
-
protection, self
-
preservation, self
-
defense, security, freedom, fina
ncial
markets…) or public perception (such as conceit, complacency, personal
privacy, happiness, identity fraud, safety, loss of control, governmental
conspiracy…). Even though an emerging technology may demonstrate the
capacity to be financially rewarding

and/or fulfill a profound need a
solution may still not be established, because the technology does not
apply to a large enough demographic. For instance, the marketing
strategy may not have included disabled individuals (a routinely
5



overlooked demographi
c). It is not until such a technology is applicable or
needed by the public at large that an implementation standard is
established.


Since biometric technologies do not currently meet the perceived needs
of the public at large, a standardized implement
ation plan has not been
conceived. I would however contend that public perception as related to
the cultural paradigm is the greatest challenge facing businesses,
managers, and society.

Foundation of the Study


This study examines the theoretical feasi
bility of enhancing assistive
technologies through the adaptation and implementation of biometric
technologies. Biometric technologies could theoretical be applied to all
areas of our earthly environment and may just become the standard
identification inte
rface between man and machine.


The information gathered from this study can be absolutely applied to
assistive technologies and in turn can be a powerful tool to aid in the
expansion of knowledge and the creation of opportunities for all
individuals w
orldwide.

Significance of the Study


The technological underpinnings of biometric technologies are some
have the most promising and life altering fundamentals in existence today.
6



Barring cultural barriers, the adaptation and implementation of biometric
s
technologies could feasibly bring about a rudimentary shift with respect to
security, access and control. Thereby, giving birth to the creation of many
new assistive technology solutions and launching the world into a new
era, an era where all things are

possible and disabilities as we know them
today have been eradicated from existence.


Biometric technologies can be adapted to areas requiring secure
access and control. Biometrics can be used to access logical assets and
to potentially facilitate abs
olute control of both logical devices and physical
components, in both the realities of the virtual and tangible worlds. In
theory, biometric technologies could be adapted to interface with
applications, personal computers, networks, accounts, human resour
ce
records, telephone system, automotive vehicles, planes, trains,
wheelchairs, exoskeleton, and could be used in the invocation of
customized profiles to enhance the mobility of people with varied ability
levels (Nanavati et al.).


An added benefit o
f to biometric technologies is that it could potentially
provide society with a feasible resolution to one of the greatest challenges
facing businesses of today. That problem is the task of business to
maintain a qualified workforce. This is primarily beca
use of the change
from an industrial workforce to a knowledge workforce and because the
7



baby boomers have only had about half as many children as their parents.

Scope of the Study


This study will center on the underlining technologies of biometrics a
nd
the existence of cultural barriers with respect to the adaptation of
biometric technologies standards within the workplace and the
international society.


All attributes of the underlining technologies and the cultural barriers
will include but not
be limited to the positives and negatives of biometric
readers, biometric characteristics, smart cards, neural interfaces,
technology standards, implementation strategies, legal issues, privacy
issues, barriers, workplace culture, government culture, civil
ian culture,
the elderly, and people with disabilities (whom have the most to gain).

Rationale of the Study


To overcome the disenabling effects of mental, physical (mobility),
structural (building), or emotional barriers as related to the access and
c
ontrol of electronic devices and technology that span the environments of
both the virtual and tangible worlds.


The societies of the world have hence, looked towards the advantages
of assistive technologies for assistance. The reality of the matter is

that
while assistive can help to overcome many mental, physical, and
emotional barriers it cannot and will not ever possess the ability to
8



overcome the reigning number one barriers confronting people with
disabilities. The reigning number one barrier has
been created by the
international society and is referred to as the cultural barriers. Cultural
barriers embody numerous complex, dynamic, and diverse challenges to
be overcome. These challenges are related to but are not limited to
cultures of the workpla
ces and societies of the international communities
(Hagner & DiLeo, 1993).


Biometric technologies will play a significant role in the eradication of
the threefold problem.

However, the best rationale of all is that to do so is
the mark of an enlighten

people and the right thing to do.

Glossary of Terms

The following are terms that will be used throughout the study.

Ability to Verify/ATV:

Is a combination of the FTE and FNMR.

Abstract Interactor:

An interactor that describes the selection, input, or
o
utput for a user interaction, without constraining the concrete form of
the interaction.

Accessibility:

The opportunity for people of any ability level to interface
with electronic devices or technology to overcome all logical and
physical barriers.

Acoust
ic Emission:

A proprietary technique used in signature
verification. As a user writes on a paper surface, the movement of the
9



pen tip over the paper fibers generates acoustic emissions that are
transmitted in the form of stress waves within the material of

a writing
block beneath the document being signed. The structure
-
borne elastic
waves behave in materials in a similar way to sound waves in air and
can be detected by a sensor attached to the writing block.

Active Impostor Acceptance:
When an impostor sub
mits a modified,
simulated or reproduced biometric sample, intentionally attempting to
relate it to another person who is an enrollee, and the person is
incorrectly identified or verified by a biometric system as being that
enrollee. Compare with 'Passive
Impostor Acceptance'.

AFIS (Automated Fingerprint Identification System):

A highly
specialized biometric system that compares a single finger image with
a database of finger images, AFIS is predominantly within law
enforcement agencies.

AIAP:

Acronym for
Alternate Interface Access Protocol.

AIAP
-
URC:

Acronym for Alternate Interface Access Protocol Universal
Remote Console.

Algorithm:

A sequence of instructions that tell a biometric system how to
solve a particular problem. An algorithm will have a finite n
umber of
steps and is typically used by the biometric engine to compute
10



whether a biometric sample and template is a match. See also
'Artificial Neural Network'.

Alternate/Abstract Interface Markup Language (AAIML):

The Alternate
& Abstract Interface Marku
p Language (AAIML) is a vehicle by which
a target conveys an abstract user interface description to a URC in
the control phase, i.e. after a session has been opened between the
URC and the target. The abstract UI description is presentation
independent and

must include all features and functions the target
provides via its default (built
-
in) user interface.

API (Application Program Interface):

A set of services or instructions
used to standardize an application. An API is computer code used by
an applicatio
n developer. Any biometric system that is compatible with
the API can be added or interchanged by the application developer.
See also Part III
Terms Related to Specific Biometric Techniques

for
'SVAPI' under 'Speaker Verification'.

Application Developer:
An individual entrusted with developing and
implementing a biometric application.

Aqueous Humor:

A transparent liquid contained in the anterior and
posterior chambers of the eye, produced by the ciliary process it
passes to the venous
system

via the canal of Schlemm.

11



Artificial Neural Network:

A method of computing a problem. An artificial
neural network uses artificial intelligence to learn by past experience
and compute whether a biometric sa
mple and template is a match.
See also 'Algorithm'.

ASIC (Application Specific Integrated Circuit):

An integrated circuit
(silicon chip) that is specially produced for a biometric system to
improve performance.

Attempt:

The submission of a biometric sampl
e to a biometric system for
identification or verification. A biometric system may allow more than
one attempt to identify or verify.

Authentication:

Is the process of validating that an individual is in fact the
person whom they claim to be.

Auto
-
correl
ation:

A proprietary finger scanning technique. Two identical
finger images are overlaid in the auto
-
correlation process, so that light
and dark areas, known as Moiré fringes, are created.

Automatic ID/Auto ID:
An umbrella term for any biometric system or
other security technology that uses automatic means to check
identity. This applies to both one
-
to
-
one verification and one
-
to
-
many
identification.

Backbone:

The main wire of a network or the wire to which the nodes of
a network connect.

12



Behavioral Biometr
ic:

A biometric that is characterized by a behavioral
trait that is learnt and acquired over time rather than a physiological
characteristic. See Part III
Terms Related to Specific Biometric
Techniques

for 'Keystroke Dynamics', 'Signature Verification' and

'Speaker Verification'. Contrast with 'Physical/Physiological Biometric'.

Bifurcation:

A branch made by more than one finger image ridge.

Binning:

A specialized technique used by some AFIS vendors. Binning is
the process of classifying finger images acc
ording to finger image
patterns. This predominantly takes place in law enforcement
applications. Here finger images are categorized by characteristics
such as arches, loops and whorls and held in smaller, separate
databases (or bins) according to their cat
egory. Searches can be
made against particular bins, thus speeding up the response time and
accuracy of the AFIS search.

Biometric:

A measurable, physical characteristic or personal behavioral
trait used to recognize the identity, or verify the claimed id
entity, of a
living person.

Biometric Application:

The use to which a biometric system is put. See
also 'Application Developer'.

13



Biometric Data:

The extracted information taken from the biometric
sample and used either to build a reference template or to
compare
against a previously created reference template.

Biometric Engine:

The software element of the biometric system, which
processes biometric data during the stages of enrolment and capture,
extraction, comparison and matching.

Biometric Identificat
ion Device:

The preferred term is 'Biometric
System'.

Biometric Sample:

Data representing a biometric characteristic of an
end
-
user as captured by a biometric system.

Biometric System:

An automated system capable of, Capturing a
biometric sample from an
end user; Extracting biometric data from
that sample; Comparing the biometric data with that contained in one
or more reference templates; Deciding how well they match; and
Indicating whether or not an identification or verification of identity has
been ac
hieved.

Biometric Taxonomy:

A method of classifying biometrics. For example,
San Jose State University's (SJSU) biometric taxonomy uses
partitions to classify the role of biometrics within a given biometric
application. Thus an application may be classifie
d as:



Cooperative vs. Non
-
Cooperative User

14





Overt vs. Covert Biometric System



Habituated vs. Non
-
Habituated User



Supervised vs. Unsupervised User



Standard Environment vs. Non Standard Environment

Biometric Technology:

A classification of a biometric sy
stem by the type
of biometric.

Booking:

The process of capturing inked finger images on paper, for
subsequent processing by an AFIS.

Capacitance:

Finger images capture technique that senses an electrical
charge, from the contact of ridges, when a finger
is placed on the
surface of a sensor.

Capture:

The method of taking a biometric sample from the end user.

Central processing unit (CPU):

The brains of the computer.


Certificate authority (CA):

The third party that issues digital certificates
and vouches

for the identity of parties involved in an online
transaction.

Certification:

The process of testing a biometric system to ensure that it
meets certain performance criteria. Systems that meet the testing
criteria are said to have passed and are certified
by the testing
organization.

15



Comparison:

The process of comparing a biometric sample with a
previously stored reference template or templates. See also 'One
-
To
-
Many' and 'One
-
To
-
One'.

Claim of Identity:

When a biometric sample is submitted to a biometric

system to verify a claimed identity.

Claimant:

A person submitting a biometric sample for verification or
identification whilst claiming a legitimate or false identity.

Clock speed:

The speed at which the CPU or microprocessor executes
instructions.

Clo
sed
-
Set Identification:

When an unidentified end
-
user is known to be
enrolled in the biometric system. Opposite of 'Open
-
Set Identification'.

CMOS (Complementary Metal Oxide Semiconductor):

A type of
integrated circuit used by some biometric systems becau
se of its low
power consumption.

Combinatorial:

The branch of mathematics concerned with analyzing
combinations of events and their associated probabilities.

Commensurability:

The universal format and length of Codes.

Concrete Interactor:

An interactor th
at describes the selection, input, or
output for a user interaction, and includes information on the visual or
non
-
visual realization of that interaction, for example a list box or a
particular speech grammar.

16



Control Phase:

The control phase is the time p
eriod in the URC
-
target
communication exchange when the URC controls the target via
AAIML.

Crossover Rate:

Synonym for 'Equal Error Rate'.

D Prime:

A statistical measure of how well a biometric system can
discriminate between different individuals. The la
rger the D Prime
value, the better a biometric system is at discriminating between
individuals.

Deep Web:

Refers to a massive trove of information stored in databases,
multimedia files and other formats that don't turn up on standard
search engine service
s.

Degrees of Freedom:

The number of statistically independent features in
biometric data.

Denial of service attack:

Occurs when hackers send thousands or
hundreds of thousands of requests to a server at the same time with
the intention of knocking it out

of service.

Discovery Phase:

The discovery phase initializes the URC to locate and
identify all available targets.

Discriminate Training:

A means of refining the extraction algorithm so
that biometric data from different individuals are as distinct as
p
ossible.

17



DNA:
DEOXYRIBONUCLEIC ACID organic chemical of complex
molecular structure that is found in all prokaryotic and eukaryotic cells
and in many viruses. DNA codes genetic information for the
transmission of inherited traits.

DPI (Dots Per Inch):

A me
asurement of resolution for finger image
biometrics.

DSV (Dynamic Signature Verification):
Synonym for 'Signature
Verification'.

Eigenface:

A method of representing a human face as a linear deviation
from a mean or average face.

Eigenhead:

The three dimen
sional version of Eigenface that also
analyses the shape of the head.

Encryption:

The act of converting biometric data into a code so that
people will be unable to read it. A key or a password is used to
decrypt (decode) the encrypted biometric data.

End

User:

A person who interacts with a biometric system to enroll or
have his/her identity checked.

End User Adaptation:

The process of adjustment whereby a participant
in a test becomes familiar with what is required and alters their
responses accordingly.

Enrollee:

A person who has a biometric reference template on file.

18



Enrollment:

The process of collecting biometric samples from a person
and the subsequent preparation and storage of biometric reference
templates representing that person's identity.

Enro
llment Time:

The time period a person must spend to have his/her
biometric reference template successfully created.

Equal Error Rate:

When the decision threshold of a system is set so that
the proportion of false rejections will be approximately equal to t
he
proportion of false acceptances. A synonym is 'Crossover Rate'.

Ergodicity:

The representative ness of sub samples.

Ethernet:

Technology standard used to link computers in local area
networks.

Extraction:

The process of converting a captured biometric s
ample into
biometric data so that it can be compared to a reference template.

Extranet:

A network linking different computer networks over the Internet.

Failure to Acquire:

Failure of a biometric system to capture and extract
biometric data.

Failure to A
cquire Rate:

The frequency of a failure to acquire.

False Acceptance:

When a biometric system incorrectly identifies an
individual or incorrectly verifies an impostor against a claimed identity.
Also known as a Type II error.

19



False Acceptance Rate/FAR:

T
he probability that a biometric system will
incorrectly identify an individual or will fail to reject an impostor. Also
known as the Type II error rate.

False Match Rate/FMR:

Alternative to 'False Acceptance Rate'. Used to
avoid confusion in applications
that reject the claimant if their
biometric data matches that of an enrollee. In such applications, the
concepts of acceptance and rejection are reversed, thus reversing the
meaning of 'False Acceptance' and 'False Rejection'. See also 'False
Non
-
Match Rat
e'.

False Non
-
Match Rate/FNMR:

Alternative to 'False Rejection Rate'. Used
to avoid confusion in applications that reject the claimant if their
biometric data matches that of an enrollee. In such applications, the
concepts of acceptance and rejection are
reversed, thus reversing the
meaning of 'False Acceptance' and 'False Rejection'. See also 'False
Match Rate'.

False Rejection:

When a biometric system fails to identify an enrollee or
fails to verify the legitimate claimed identity of an enrollee. Also k
nown
as a Type I error.

False Rejection Rate/FRR:

The probability that a biometric system will
fail to identify an enrollee, or verify the legitimate claimed identity of
an enrollee. Also known as a Type I error rate.

20



Failure to Acquire/FTA:

Represents th
e probability that the user
biometric characteristic is either damage, flawed, and/or not
presented in the correct manner.

Failure to Enroll/FTE:

Represents the probability that a user failed to
enroll into the biometric system.

FAS:

Fused Accessible Solut
ion.

Field Test:

A trial of a biometric application in 'real world' as opposed to
laboratory conditions.

Filtering:

A specialized technique used by some AFIS vendors. Filtering
is the process of classifying finger images according to data that is
unrelated

to the finger image itself. This may involve filtering by sex,
age, hair color or other distinguishing factors.

Fixed
-
Text System:

The preferred term is 'Text
-
Dependent System'.

Goats:

Biometric system end users whose pattern of activity when
interfacin
g with the system varies beyond the specified range allowed
by the system, and who consequently may be falsely rejected by the
system.

Genetic Penetrance:

The degree to which characteristics are passed
from generation to generation.

Hamming Distance:

The

number of disagreeing bits between two binary
vectors. Used as measure of dissimilarity.

21



Identification/Identify:
The one
-
to
-
many process of comparing a
submitted biometric sample against all of the biometric reference
templates on file to determine wheth
er it matches any of the
templates and, if so, the identity of the enrollee whose template was
matched. The biometric system using the one
-
to
-
many approach is
seeking to find an identity amongst a database rather than verify a
claimed identity. Contrast wi
th 'Verification'.

Impostor:

A person who submits a biometric samples in either an
intentional or inadvertent attempt to pass him/herself off as another
person who is an enrollee.

In
-
House Test:

A test carried out entirely within the environs of the
biom
etric developer, which may or may not involve external user
participation.

Instant Messaging:

A system in which words typed on a computer
appear almost simultaneously on the computer screens of other
people.


Interactor:

An abstract or concrete user inter
face element that describes
a choice for the user to make, some input to obtain from the user, or
some output to convey to the user.

Invisible Web:

see DEEP WEB.

22



Iris Features:

A number of features can be found in the iris. These are
named corona, crypts,
filaments, freckles, pits, radial furrows and
striations.

Linux:

An operating system developed by volunteer programmers around
the world as an alternative to Microsoft Corp.'s Windows. In addition
to not being a Microsoft product, the other big selling poi
nt of Linux is
that it is open
-
source software.


Live Capture:

The process of capturing a biometric sample by an
interaction between an end user and a biometric system.

Live Scan:

The term live scan is typically used in conjunction with finger
image techn
ology. Synonym for 'Live Capture'.

Local area network (LAN):

A computer network with a reach limited to an
office, a building or a campus.

Managed service provider (MSP):

Any company that offers outsourced
hosting and management of Web
-
based services, app
lications and
equipment.


Match/Matching:
The process of comparing a biometric sample against a
previously stored template and scoring the level of similarity. A accept
or reject decision is then based upon whether this score exceeds the
given threshold.

23



Media Access Control (MAC) Address:
On a local area network (
LAN
)
or other network, the MAC (Media Access Control) address is your
computer's unique hardware numbe
r. (On an
Ethernet

LAN, it's the
same as your Ethernet address.) When you're connected to the
Internet from your computer (or
host

as the Internet protocol thinks of
it), a correspondence table relates your
IP address

to your computer's
physi
cal (MAC) address on the LAN. The MAC address is used by the
Media Access Control sub layer of the Data
-
Link Layer (
DLC
) layer of
telecommunication
protocol
. There is a different MAC sub layer for
each physical device type. The other sub layer level in the DLC layer
is the Logical Link Control sub layer.

Microprocessor:

See

Central Processing Unit.

Minutiae:

Small details found in finger images such as ridge endings or
bifurcations.

Minutiae Points:

are local ridge characteristics that occur at either a ridge
bifurcation or a ridge ending.

MOC (Match
-
On
-
Card):

technology of
fered in certain smart cards with
which a biometric template comparison is carried out within the
confines of the card.

24



Morphogenesis:

The process of shape formation: the processes that are
responsible for producing the complex shapes of adults from the
si
mple ball of cells that derives from division of the fertilized egg.

Neural Net/Neural Network:

Synonym for 'Artificial Neural Network'.

OEM (Original Equipment Manufacturer/Module):

A biometric
organization (Manufacturer), which assembles a complete biome
tric
system from parts; or a biometric Module for integration into a
complete biometric system.

One
-
To
-
Many:

Synonym for 'Identification'.

One
-
To
-
One:

Synonym for 'Verification'.

Open
-
Set Identification:
Identification, when it is possible that the
indi
vidual is not enrolled in the biometric system. Opposite of 'Closed
-
Set Identification'.

Open source:

Technology with an underlying programming code that is
free for all to use and alter. A band of programmers, technologists
and some companies around the w
orld are advocating open
-
source
technology. The goal is to develop technology that is compatible with
other technologies.


Optical:

Finger images capture technique that uses a light source, a
prism and a platen to capture finger images.

25



Out Of Set:

In ope
n
-
set identification, when the individual is not enrolled
in the biometric system.

Passive Impostor Acceptance:

When an impostor submits his/her own
biometric sample and claiming the identity of another person (either
intentionally or inadvertently) he/sh
e is incorrectly identified or verified
by a biometric system. Compare with 'Active Impostor Acceptance'.

Patch:

Software program used to fix a hole or bug in a software
application. Companies offer "patches" free to customers when
vulnerabilities or prob
lems are discovered in the products they sell.


Pectinate Ligaments:

The network of fibres at the iridocorneal angle
between the anterior chamber of the eye and the venous sinus of the
sclera; it contains spaces between the fibres that are involved in
drai
nage of the aqueous humor, and is composed of two portions: the
corneoscleral part, the part attached to the sclera, and the uveal part,
the part attached to the iris.

Performance Criteria:

Pre
-
determined criteria established to evaluate the
performance of

the biometric system under test.

Photonics:

Technology used to transmit voice, data and video via light
waves over thin strands of glass.


Physical/Physiological Biometric:
A biometric, which is characterized
by a physical characteristic rather than a be
havioral trait. See Part III
26



Terms Related to Specific Biometric Techniques

for 'Body Odor', 'Ear
Shape', 'Face Recognition', 'Finger Geometry', 'Finger Image', 'Hand
Geometry', 'Iris Recognition', 'Palm', 'Retina', 'Speaker Verification'
and 'Vein check'.

Contrast with 'Behavioral Biometric'.

PIN (Personal Identification Number):

A security method whereby a
(usually) four
-
digit number is entered by an individual to gain access
to a particular system or area.

Platen:

The surface on which a finger is placed

during optical finger
image capture.

Plug
-
in:

Software programs that make a Web browser run better,
including allowing the downloading of information on the Internet.


Presentation
-
Independent Template:

A form of UIID. It describes a
mapping from a user i
nterface socket to a structured set of abstract
interactors. This mapping provides access to all of the commands and
readable data points within the user interface socket.

Privacy:
The degree to which an individual can determine which personal
information
is to be shared with whom and for what purpose. Always a

concern is when an authorized users pass confidential information to
another vendor or government agency.
Public key infrastructure
(PKI):

Refers to the framework, including digital certificates and
27



certificate authorities, used to securely conduct and authenticate
online transactions.

Reasonable Accommodation:

Include those structural and technological
modifications that do not impose an undue hardship on the employer.

Receiver Operating Curves:

A gr
aph showing how the false rejection
rate and false acceptance rate vary according to the threshold.

Recognition:

The preferred term is 'Identification'.

Response Time:
The time period required by a biometric system to return
a decision on identification
or verification of a biometric sample.

Ridge:

The raised markings found across the fingertip. See also 'Valley'.

Ridge Ending:
The point at which a finger image ridge ends.

Risk Assessment Methodology (RAM):

A three
-
step method of
assessing the risk of w
hether to endorse or veto the relevance of a
proposed solution.

Routers:

Devices that act as traffic cops for computer data on the
Internet.

Security:

The protection of data against unauthorized access. Programs
and data can be secured by employing a care
fully designed and
planned authentication method.


Semantic Web:

A vision or concept articulated by some computing
leaders
--

including Tim Berners
-
Lee,

recognized as the creator of
28



the World Wide Web
--

of how computer programs and technologies
can be use
d to semantically structure, describe, search and interpret
documents and data on the Web. This concept envisions the Web
evolving from an HTML
-
based one to the semantic Web.

Session:

A continuous period over which a user is engaged with the
target.

Short
message service (SMS):

Brief text messages that are transmitted
via mobile phones.

Software agent(s):

"Intelligent" software programs that perform tasks,
search and retrieve information a user requires from databases and
computer networks.

Supplemental Re
sources:

Interpretation and translation resources that
may be used in building a user interface. These resources include text

for labeling interface elements, help text, translations into other
languages, and icons, graphics or other multi
-
media elements.

Target:

The target is a device (e.g. VCR) or service (e.g. online phone
directory) that the user wishes to use.

Target
-
Class Template:

A UIID that can be mapped to the user interface
socket of any target of a certain class such as microwave ovens or
televi
sions.

29



Technology Access Barriers (TAB):

A structure or object that impedes
free bi
-
directional in parallel access (movement) to technology.

Template/Reference Template:
Data, which represents the biometric
measurement of an enrollee used by a biometric sy
stem for
comparison against subsequently, submitted biometric samples.

Thermal:

A finger image capture technique that uses a sensor to sense
heats from the finger and thus captures a finger image pattern.

Third
-
generation networks):

Much
-
hyped technology

that promises to
enable high
-
speed downloading of data, including videophone
service, and worldwide compatibility.


Third Party Test:

An objective test, independent of a biometric vendor,
usually carried out entirely within a test laboratory in controlled

environmental conditions.

Threshold/Decision Threshold:
The acceptance or rejection of biometric
data is dependent on the match score falling above or below the
threshold. The threshold is adjustable so that the biometric system
can be more or less stric
t, depending on the requirements of any
given biometric application.

Throughput Rate:

The number of end users that a biometric system can
process within a stated time interval.

30



Trojan horse:

Malicious code that is often hidden in e
-
mail attachments
that
once activated can be used to steal or destroy programs and
data on a computer.

UBID:

Acronym for Universal Biometric Identification.

UI:

Acronym for User Interface.

UIID:

Acronym for User Interface Implementation Description.

Ultrasound:

A technique for
finger image capture that uses acoustic
waves to measure the density of a finger image pattern.

Universal Remote Console (URC):

The URC is a device or software
through which the user accesses a target. The URC complies with the
AIAP
-
URC specification and
is capable of rendering any AAIML
specified user interface. It is “universal” in the sense that it can be
used to control any AIAP
-
URC compliant target. It is assumed that
users will choose a URC capable of meeting their personal interaction
requirements.

URC:

Acronym for Universal Remote Console.


User:

The client to any biometric vendor. The user must be differentiated
from the end user and is responsible for managing and implementing
the biometric application rather than actually interacting with the
bio
metric system.

31



User Interface Instance:

A UIID that completely describes a user
interface and has been built and made available in advance of the
user’s session with the target.

User Interface Instantiation:

A UIID that completely describes a user
interfa
ce and has been dynamically derived from a presentation
-
independent template during the user interface construction phase of
a user’s session with a target.

User Interface Socket:

A low level description of a specific target. It
describes the functionality

and state of the target as a set of data
points and commands.

Validation:
The process of demonstrating that the system under
consideration meets in all respects the specification of that system.

Valley:

The corresponding marks found on either side of a f
inger image
ridge.

Verification/Verify:
The process of comparing a submitted biometric
sample against the biometric reference template of a single enrollee
whose identity is being claimed, to determine whether it matches the
enrollee’s template. Contrast w
ith 'Identification'.

Web bugs:

Invisible files hidden on Web pages to help marketers
determine who has seen their ads.

Webcast:

Audio, video or both broadcast on the Web.

32



Web clipping:

Shortened versions of Web pages designed to fit and be
displayed on

the small screens of handheld devices.


Web services:
A catch
-
all term describing a trend in which services are
delivered over the Internet, or the Internet is used to automate tasks.

Wide area networks (WANs):

Computer networks, spanning great
distances

that are connected to each other.

Wi
-
fi:

A wireless technology standard that was formerly called 802.11b.
The technology allows people to connect to networks using simple
radio antennas in their laptops or desktop PCs.

Worm:

A computer program that repl
icates and spreads from computer to
computer via e
-
mail.

WSQ (Wavelet Transform/Scalar Quantisation):

A compression
algorithm used to reduce the size of reference templates.

X Internet:

Buzzword coined by Forrester Research Inc., with the X
standing for "e
xecutable" or "extended" Internet in which non
-
PC
devices and consumer products, including cell phones, televisions,
cars and refrigerators, are linked to the Internet.


Zero Effort Forgery:

An arbitrary attack on a specific enrollee identity in
which the
impostor masquerades as the claimed enrollee using his or
her own biometric sample.

33



Overview of the Study


This research paper will attempt to show that within the international
culture of today’s information age there exist a threefold (interconnected
)
problem to be addressed with respect to the existence of a secure access
methodology to electronic devices and technology for people with variable
abilities. F
urthermore, this study will analyze the theoretical aspects,
concepts, and barriers (logical, p
hysical, cultural, and tangible) related to
the adaptation and implementation of biometric technologies to people
with of variable abilities. This study must and will embody the
characteristics of universal design
philosophies
.











34







Review of Rel
ated Literature

Chapter 2



From a multi
-
dimensional perspective there are a multitude of related
theories, concepts, practices (strategies), and technologies from both
printed and electronic mediums that apply to each individual facet of
assistive tec
hnologies, biometric technologies, smart card technologies,
universal design, neural control, privacy issues, legal issues, security,
accessibility, and the ever
-
present cultural barriers of society. More to the
point, the related literature will link the
theories, concepts, and practices of
the aforementioned facets to the adaptation of biometric technologies to
people with disabilities. Thereby, proving that biometric technologies can
indeed be adapted to people with disabilities as the supreme assistive
technology.


The paragraphs that follow will only be a synopsis of the dominant
philosophies as related to the many facets of implementation and
adaptation of biometric technologies to people with disabilities. Hence, the
35



following paragraphs will assi
st to establish a literary framework of cultural
theories, societal concepts, implementation practices, and technology
standards.

Mainstream Biometric Technologies


The function of a biometric technologies authentication system is to
facilitate control
led access to applications, networks, personal computers
(PCs), and physical facilities. A biometric authentication system is
essentially a method of establishing a person’s identity by comparing the
binary code of a uniquely specific biological or physica
l characteristic to
the binary code

of an electronically stored characteristic called a
biometric. The defining factor for implementing a biometric authentication
system is that it cannot fall prey to hackers; it can’t be shared, lost, or
guessed. Simply p
ut, a biometric authentication system is an efficient way
to replace the traditional password based authentication system
(Ashbourn, 2000).

Emerging Biometric Technologies


The neural waves emanate from a subject’s brain in the form of
brainwaves or bi
oelectrical impulses. To further iterate this, please refer to
an article called “Monkey Brain Operates Machine” published on the BBC
News website (
http://news.bbc.co.uk
/hi/english/sci/tech/
newsid_1025000/1025471.stm
). This is not the first article or paper of this
36



type, to promote the attributes of neural control. On the contrary, there
have been countless papers and articles released from multiple
universities and col
leges in an attempt to document their research.


IBVA Technologies (www.ibva.com) is the first company to
commercialize the distribution of a neural control device. Essentially, a
neural control device is a system that is designed to sense and analyze
a
persons’ neural waves and then interfaces with a computer to allow the
user to navigate (control) with brainwaves; neural control would be
analogous to the use of a human hand. The problem is that the
technology must be customized for each user and is th
erefore not easily
adaptive to each individual. The researcher has speculated that
brainwaves are unique and could emerge from neural devices as the
newest biometric. The title of the paper is "Let Me In!!! (Biometric Access
& Neural Control)” and was publ
ished (November 2001) at
http://www.icdri.org/biometrics/let_me_in.htm

by the International Center
for Disability Resources on the Internet. Republished (March 2002) at
http://www.nextinterface.net/biometricsandsecurity

and (June 2002) at
http://www.findbiometrics.com/Pages/letmein.html
.


Corporate and university website are of a tremendous source of
information on emerging technologies. The corporate website of PosID,
Inc. (
http://www.posidinc.com
) is an excellent source of information on an
37



emerging biometric technique known as "Infrared Imaging And Pattern
Recognition" and it should be as they hold the patented (#5,351,303).


Radio Fre
quency Identification (RFID)


As indicated by the white paper composed by Accenture (2001), a
RFID employs radio frequency communications to exchange data
between a portable memory device and a host computer. An RFID
typically consists of a tag, label,

or PCB for storing data, an antenna to
communicate, and a controller. RFID’s can either be active (battery) or
passive (no battery) and can be produce with read/writer (two
-
way) or
read only (one
-
way) capabilities. Additionally, an RFID is a suitable
meth
od of replacing bar code.


Clark Richter (1999) of Intermec Technologies Corporation author of a
white paper titled “RFID: An Educational Primer” he has in general terms
explained basic RFID concepts with respect to RFID technology, markets
and applica
tions.


Editor Chris Corum (2002) of AVISAIN Inc., authored “Why RFID is the
right choice for personal ID”. In this newsletter the author declares that an
RFID card is the best and most secure method of identification. An RFID
card is a bare bones vers
ion of a contactless smart card.

38



Smart Card Technologies


The most common standardized encryption method used to secure a
company’s infrastructure is the Public Key Infrastructure (PKI) approach.
This method consists of two keys with a binary string ra
nging in size from
1024
-
bits to 2048
-
bits, the first key is a public key (widely known) and the
second key is a private key (only known by the owner). However, the PKI
must also be stored, and inherently, it too can fall prey to the same
authentication lim
itation of a password, PIN, or token. It too can be
guessed, lost, stolen, shared, hacked, or circumvented; this is even
further justification for a biometric authentication system (Corcoran et al.).


Per Walder (1997), the best overall way to secure a
n enterprise
infrastructure, whether it be small or large is use a smart card. A smart
card is a portable device with an embedded central processing unit (CPU).

The smart card can either be fashioned to resemble a credit card,
identification card, radio fr
equency identification (RFID), or a Personal
Computer Memory Card International Association (PCMCIA) card
(Biocentric Solutions Inc., n.d.). The smart card can be used to store data
of all types, but it is commonly used to store encrypted data, human
resou
rces data, medical data, and biometric data (template). The smart
card can be access via a card reader, PCMCIA slot, or contactless
39



proximity reader; it is therefore in compliance with section 508 of the
Americans with Disabilities Act (ADA) (Walder, 1997)
.


A smart card is the best storage medium to use when implementing a
biometric authentication system; only by the using a smart card can an
organization satisfy all security and legal requirements (Biocentric
Solutions Inc., n.d.). Corcoran et al. (1
999) stated, “This process
irrefutably authenticates the person presenting the card as the same
person to whom the cryptographic keys belong and provides the
necessary tight binding between cryptographic key storage and the
authorized user of the cryptogra
phic keys.” (p. 5).


Smart Card Alliance (
http://www.smartcardalliance.org
) is a not
-
for
-
profit organization that is known among the smart card industry as the
premiere source of smart card research dat
a and reports. The mission of
the Smart Card Alliance is to promote the acceptance of smart card
technologies. The mission of the Smart Card Alliance would be analogous
to the mission of the Biometrics Consortium, which is to promote
acceptance of biometri
cs.


The premiere expert on the use of RFIDs and smart cards as assistive
technology to aid people with disabilities is Dr. John Gill, OBE FIEE of the
Royal National Institute for the Blind. Dr. Gill has participated in numerous
studies and published m
ultiple papers that are of great significance to this
40



study. Other than the historical documentation that has been contributed by
Dr. Gill, the research has also been participating in a one
-
on
-
one
conversation with Dr. Gill via email exchange.

Assistive T
echnologies


Assistive technologies play a major role in school, work, and the
society at large. With respect to authors of assistive technology books, the
quantity of material is scarce; on the other hand the quality of the
available material is supre
me. “
Assistive technology: A resource for school,
work, and community”, was composed by Flippo, Inge, & Barcus (1995) and
is one such artistic production.


The fundamental development and foundation of assistive technologies
are dictated by legislation

and federal policy. The legislation and policies
have also set the stage for standards associated to the application of
communication technologies, sensory impairment technologies, mobility,
and strategies for schools, the workplace, and society (Flippo,
Inge, &
Barcus, 1995).



While published books are scarce, there are many more source of
literature related to assistive technologies from government and nonprofit
organizations, both domestic to the United States and international.
The
accessible futu
re

was authored by the National Council on Disability [NCD]
41



(2001, June 21) and is a publication that attempts to establish that an
assistive technology framework is a civil rights concept.


As implied by Heldrick (1999), the employment of assistive te
chnologies
within companies has also created a multitude of developmental staffing
and creative financing issues.

Cultural Barrier (Disabled & Elderly)


The post World War I theory or concept of disability was perceived as
a medical condition (mental,
physical, or emotional) that lead to the