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

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Signature 111 (E)

Simple - Complex

Illegible - Legible

Sloppy - Neat

Straight - Curved

Common - Unique

(a) - (b)

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Signature 112 (E)

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Illegible - Legible

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(a) - (b)

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Signature 114 (E)

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Signature 116 (E)

Simple - Complex

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Signature 104 (C)

Simple - Complex

Illegible - Legible

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(a) - (b)

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Signature 119 (E)

Simple - Complex

Illegible - Legible

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(a) - (b)

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Signature 122 (E)

Simple - Complex

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Sloppy - Neat

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(a) - (b)

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Signature 126 (E)

Simple - Complex

Illegible - Legible

Sloppy - Neat

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Common - Unique

(a) - (b)

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Signature 128 (E)

Simple - Complex

Illegible - Legible

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(a) - (b)

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Signature 129 (E)

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Illegible - Legible

Sloppy - Neat

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(a) - (b)

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Signature 132 (E)

Simple - Complex

Illegible - Legible

Sloppy - Neat

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(a) - (b)

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Signature 136 (E)

Simple - Complex

Illegible - Legible

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(a) - (b)

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Signature 140 (E)

Simple - Complex

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Signature 152 (E)

Simple - Complex

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Signature 143 (E)

Simple - Complex

Illegible - Legible

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(a) - (b)

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Signature 173 (E)

Simple - Complex

Illegible - Legible

Sloppy - Neat

Straight - Curved

Common - Unique

(a) - (b)

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Signature 183 (E)

Simple - Complex

Illegible - Legible

Sloppy - Neat

Straight - Curved

Common - Unique

(a) - (b)

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Signature 184 (E)

Simple - Complex

Illegible - Legible

Sloppy - Neat

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(a) - (b)

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Signature 190 (E)

Simple - Complex

Illegible - Legible

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(a) - (b)

Initial Results from the first survey

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

Design and Testing

Ideation 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

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

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