1. How Hard is a Signature to Forge?
Joseph O’Neill, Dr. Stephen Elliott, Dr. Richard Guest, , Kevin O’Connor
The purpose of this study is to take the idea of forging signatures from one that is centered on the experience of
the forger, and looking at the characteristics of the signature itself. This was done by collecting data in the form of
multiple surveys from a selected group of semi-experts in the field of biometrics. These surveys were created to
focus on specific aspects of the signature that forgers would use to replicate the signature. The measures were
then correlated to determine the difficulty level of the survey. Many of the other features were studied to find
different results as well. The underlying thesis is before generating an impostor distribution from forgers, you
should examine the forgers perception of difficulty.
1. Next stage is examine
correlation between the
perceived attributes and the
extracted features from the
signature.
2
PERF
Overview
3
PERF
Phase 2
Initial Survey Question
Initial steps were to assess the opinion of a set of non-
professional forgers on a signature.
After initial analysis, the metrics were further refined and
an additional survey was created, and tested on a Likert
Scaler
0
2
4
6
8
10
12
14
16
18
Signature 111 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
18
Signature 112 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
5
10
15
20
25
Signature 114 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
5
10
15
20
25
Signature 116 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
Signature 104 (C)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
Signature 119 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
Signature 122 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
Signature 126 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
Signature 128 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
Signature 129 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
18
Signature 132 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
Signature 136 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
Signature 140 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
18
Signature 152 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
Signature 143 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
18
20
Signature 173 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
Signature 183 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
Signature 184 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
0
2
4
6
8
10
12
14
16
18
20
Signature 190 (E)
Simple - Complex
Illegible - Legible
Sloppy - Neat
Straight - Curved
Common - Unique
(a) - (b)
Initial Results from the first survey

2. Impact of Age and Gender on Fingerprint Recognition
Systems
Lindsay Sokol, Carson Weaver, Cassandra Harrell, Roy Mills, Brandon Galbreth, Steve McKinney, Kevin
O’Connor, Stephen Elliott
The purpose of this study is to investigate the impact of subject age and gender on fingerprint performance,
quality, and image characteristics. Previous research has shown that both age and gender impact fingerprint
image quality and performance. By identifying problem populations, recommendations can be made to improve
the interaction for these populations, in turn improving the performance of the entire system. This study will
use previously collected data using a ten-print, live-scan fingerprint sensor.
Previous Research Image Quality
Preliminary Results
1. Examine performance if gender and age at various
levels
2. Examine the role of entropy and gender
5
Overview
6
Phase 2
Data Cleaning Challenges
Paper Ref. Performance Henry
Classification
Image Quality Minutiae Ridge Width Entropy
[2] Difference
F better than
M
Difference
M better
than F
No
difference
[3] No
difference
[4] Difference
[5] Slight
Difference
[6] Slight
Difference
F better than
M
Slight
Difference
Different,
except for RI
M better
than F
No
Difference
except for
RR
This
research
 Phase 2  Phase 1  Phase 1  Phase 2
Image Quality variables are broken down into different
categories:
• Good – represents the part of the image where
minutiae points can be reliably extracted
• Poor – broken and smudged pixels
• Light – a count of light areas
• Dark – a count of dark areas
• Minutiae – the number of minutiae found in the central
area of the fingerprint
Typically, we have not analyzed image quality as part of
our performance testing previously.
Design of Experiments class
examined the dataset as part
of the course.
The datasets were skewed –
thumbs contributed to some
of the image quality issues –
especially in Light, Dark,
Good, Poor.
The group had to understand
distribution and had to make
suggestions about the data.
Understanding and cleaning
the data took the majority of
time in Phase 1.
Hypothesis Performance Image Quality Minutiae Core Delta
No difference in
core/delta
across gender
P=0.000
No difference in
core/delta
across age(h)
P=0.000
No difference in
core/delta
across age(m)
P=0.000
No difference in
minutiae across
gender
P=0.365
No difference in
image quality
across gender
P=0.000
No difference in
image quality
across age(h)
P=0.000
No difference in
image quality
across age(m)
P=0.000
With
respect to
gender
Performance Henry
Classification
Image Quality Minutiae Ridge Width Entropy Core Delta
This
research
 Phase 2 Difference
(global)
 Phase 2
examine
finger
location.
No
difference
 Phase 2 Difference

3. Design and TestingIdeation
Our original concept was shamelessly stolen from the
movie “Men In Black”, where fingerprint verification is
done using a ball-shaped scanner, scanning all fingers at
the same time. Studies have shown user comfort
improves scan quality, so we decided to adapt the
existing fingerprint scanner
to improve ergonomics,
increase throughput time,
and see an improvement
in image quality as an
additional benefit.
Measurement & Framework
Overview
Optimizing Interaction Time For Fingerprint Verification
Thomas Cimino Brandon Hilts Chris Clouser
1Biometric Standards, Performance, and Assurance Laboratory, Department of Industrial Technology
2Department of Psychological Sciences, Purdue University
Variables Of Interest
10-Print Fingerprint Scanner
Human Interaction
&Observation Area
Analysis
Usability Analysis
Statistical Data Comparing
Image QualitySatisfaction
•Questionnaire
oUser Feedback
Efficiency
• TaskTime
Effectiveness
• Number of Errors
Fingerprint Image Quality Analysis Biometric System Performance
• The initial goals of this project were to decrease the amount of time a user spends going through a fingerprint scan for normal uses (border control,
facility access, etc.) without losing any of the image quality. It is our hypothesis that we can reduce overall user interaction times by approximately one-
third by adopting this design, as it will eliminate the user needing to reposition the hands once they begin the scanning process.
EAF-3AD

4. Ergonomic Improvements to Hand Geometry Readers
Ross Barbish, Chuck Oliver, Rob Larsen, Narut Chitrudi-Amphai, Markus Jones, Tera Engle, Kevin
O’Connor, Stephen Elliott
Prototype #1 Prototype #2 Prototype #3
The purpose of this study was to weigh the objective performance decrease with the subjective comfort increase
when ergonomic accessories were attached to the surface of the hand geometry readers. These accessories are
correctly assumed to have a negative effect on performance, but the question is whether or not the degree to
which performance is decreased is acceptable or not. Our research has found that the performance decrease is
small enough that these accessories are viable options to improve the ergonomics of these devices. There also
is a near consensus among users as to which accessories are more comfortable. This is exciting as future
research and development can focus on the material and comfort of these accessories.
Identification of Variables
Initial Phase 1 results (non MSD)
Devices and Prototypes
Design of Experiment
1. Next stage is to test the three different prototypes in a
scenario and operational environment
2. Prototypes will be deployed on the door of Knoy 378
and in a test cell in MGL testing lab
3. MSD population will be recruited after IRB approval
5
Overview
6
Phase 2
• Scores were relatively stable with and without ergonomic attachments.
• Minimal fluctuations of our average scores between test runs.
• Ergonomic improvements placed underneath the hand definitely a possibility.
• Hand scores will be collected from a randomly selected group of
MSD and Non-MSD subjects on all 3 prototypes and without
prototype
• Readings on each will be collected for each participant.
• A comfort index score will be collected for each reading.
Run Chart
Comfort index – not
used in phase 1, but will
be used in phase 2

5. Understanding Environmental Conditions
Charles Belville, Jason Wintz, Andrew Thomas, Stephen Elliott, Mitch Mershon
The purpose of this study is to measure the environmental conditions in a testing lab, and to provide guidance to
ISO /IEC JTC 1SC 37 working Group 5. Biometric technologies are impacted by environmental conditions – for
example face recognition and lighting. However, no methodology exists to measure environmental conditions in a
biometric testing lab. The output of this project will be to contribute documents and test methodologies to SC 37
as well as implement environmental monitoring for data collection in the Spring.
1. Next stage is to replicate the study in Knoy 378
2. Provide guidance to the biometric community on how to setup an environmental study
3. Contributions will include input to ISO/IEC JTC 1 SC37
4. Continue with the teleconferences with the Spanish editorial team
8
PERF
Overview
9
PERF
Phase 2
Preliminary Test Design
MGL B307 – 30 (4’X4’) Zones
Test Period in each Zone: 2 Hours
Sampling Rate: 120 seconds
.
There will be a total of three different tests performed in MGL
B307.
The first test will record:
• illumination,
• temperature,
• humidity,
• pressure
The second test will record sound at rest occupancy state
The third test will record sound at operational occupancy
state.
Test Plan
1. Setup EN300 data logging to SD card
2. Setup SD700 data logging to PC via RS232
3. Begin data collection in Zone 1
1. Ensure lights are stable (approx: 6 minutes)
2. Ensure room is empty of all personnel
4. Move data collection tripod to each zone every two
hours.
5. Compile data from each zone once complete
Other Zone Results
Zone Results