CORE ZOO

INTRODUCTION

•Will biometric characteristics within a zoo menagerie change with the addition of good and bad quality biometric enrollments?

RESEARCH QUESTION/HYPOTHESIS

•Biometric menageries are used to classify subjects based on biometric matching to their own template and to others

•There are many different types of zoo’s, like the Doddington zoo and the Dunstone and Yager zoo, and they all differ from each other

STATEMENT OF THE PROBLEM

LITERATURE REVIEW

•Biometrics refers to recognizing individuals based on behavioral and physical characteristics such as fingerprints and facial features

•These biometrics can be used to identify and relate one’s features to themselves and to others

BIOMETRICS

•Fingerprints are more commonly used over every other type of biometric, especially when comparing with zoo menageries

•Fingerprints have many “minutiae points” across the surface of the finger which are used to compare and identify who a fingerprint belongs to

FINGERPRINTS

• Quality can be tested by a number of different factors, the fidelity image, the utility image, and the character image

• Fidelity checks the degree of similarity between the sample and its user

• Utility assesses the overall impact on the system

• Character represents features from a biometric sample

• Higher quality images tend to perform better in a recognition system

• High quality fingerprints have well-defined ridge structures, good global and local features as well as no abrasions, residues, cuts, or creases from age [4]

FINGERPRINT QUALITY

•These zoo menageries are created by matching a person’s biometric sample against a population. If they are not the same person’s fingerprint it is given an imposter or genuine score that describes them as imposters or genuine

•These scores determine your zoo classification

IMPOSTER AND GENUINE SCORES

• Zoo menageries were created because there are still flaws within biometrics

• They characterize people as different animals depending on how well their fingerprint compares to another

• There are two main types of zoo menageries, the Doddington zoo and the Dunstone Yeager zoo

ZOO MENAGERIES

• Measures individual scores based on how well a user matches to those in a specific dataset [8]

• Characterizes people as sheep, wolves, goats, or lambs

• Sheep match well with their own template

• Wolves match well with others

• Goats don’t match themselves well

• Lambs match with other people but not themselves

DODDINGTON ZOO

• The Dunstone and Yager Zoo is created by matching a person’s biometric sample against the data set

• Measures the relationship between a user’s genuine and impostor match results [5]

• Characterizes people as worms, phantoms, chameleons, and doves

• Worms match to others well but not to themselves

• Phantoms don’t match well with anybody

• Chameleons match to themselves and others well

• Doves match to themselves well but not to others

DUNSTONE AND YAGER ZOO

•The subject is given an average genuine based on their match results to themselves and an impostor match score based on their match results to others

•These scores determine what zoo classification each subject belongs in

GENUINE/IMPOSTOR SCORES

Phantoms

WormsChameleon

s

Doves

•Davis Wittman did a study that shows that zoo animals do exist in biometric measurements

•Found outliers in facial recognition that closely resemble other people as well as ones that do not match well to themselves

DOES A ZOO EXIST?

•Wittman et al [6] did a study that shows that zoo animals do exist in biometric measurements

•Found outliers in facial recognition that closely resemble other people as well as ones that do not match well to themselves

WEAKNESS OF THE ZOO

•Best possible users

•GH IL

DOVES

GH

GL

IH

IL

Top 25%

Bottom 25%

Genuine Impostor

•Worst possible users

•GL IH

WORMS

GH

GL

IH

IL

Top 25%

Bottom 25%

Genuine Impostor

•Appear similar to others yielding high match scores

•GH IH

CHAMELEONS

GH

GL

IH

IL

Top 25%

Bottom 25%

Genuine Imposture

• Low matching scores regardless of compared subject

• GL IL

PHANTOMS

GH

GL

IH

IL

Top 25%

Bottom 25%

Genuine Imposture

METHODOLOGY

• Core – D1 – Original dataset from O’Connor’s thesis

• Dataset 2 – D2 – additional data set from Benny

• Dataset 3 – D3 – additional data set from DHS

• Top- Top 25% of images in terms of quality

• Bottom – Bottom 25% of images in terms of quality

CLASSIFICATIONS OF DATA SETS

• The datasets were combined into groups as shown below:

1. Core plus dataset 2 top as well as core plus dataset 2 bottom (C+D2T and C+D2B)

• Results are in one table

2. Core plus dataset 3 top as well as core plus dataset 3 bottom (C+D3T and C+D3B)

• Results are in one table

3. Core plus dataset 2 + 3 top (C+D2T+D3T)

4. Core plus dataset 2 + 3 bottom (C+D2B+D3B)

• Results for 3 and 4 are in one table

METHODOLOGY

HARDWARE USED

•Crossmatch Guardian

• Fingerprint sensor

•Filemaker

• Database of Samples

•OWR Bio-Metrics

• Used to check zoo placement

•Megamatcher

• Fingerprint matching and quality scores

SOFTWARE USED

RESULTS

•154 subjects in Core

•17 subjects added from Dataset 2 for top and bottom

•20 subjects added from Dataset 3 for top and bottom

RESULTS

Core + Bottom 25% Core Core + Top 25%

IDClassificati

on IDClassificati

on IDClassificati

on

358Normal 358Phantoms 358Phantoms

652Normal 652Phantoms 652Normal

677Normal 677Phantoms 677Normal

697Normal 697Phantoms 697Normal

704Normal 704Doves 704Normal

721Normal 721Doves 721Normal

724Chameleons 724

Chameleons 724Normal

741Normal 741Normal 741Worms

742Normal 742Phantoms 742Phantoms

743Normal 743Normal 743Chameleons

747Normal 747Phantoms 747Normal

839Normal 839Phantoms 839Normal

RESULTS OF CORE + D2

Core + Bottom 25% Core

Core + Top 25%

ID Category ID Category IDCategory

239 Worm 239 Normal 239 Normal

302 Dove 302 Dove 302 Normal

358 Normal 358 Phantom 358Phantom

359Chameleon 359

Chameleon 359 Normal

724Chameleon 724

Chameleon 724 Normal

726Chameleon 726

Chameleon 726 Normal

737 Worm 737 Worm 737 Normal

740 Phantom 740 Normal 740Phantom

743Chameleon 743 Normal 743 Normal

775 Normal 775 Normal 775Phantom

839 Normal 839 Phantom 839Phantom

RESULTS OF CORE + D3

RESULTS OF CORE + D2 + D3Core + Bottom

25% CoreCore + Top

25%

ID Category ID Category ID Category

302 Dove 302 Dove 302 Normal

358 Normal 358 Phantom 358 Phantom

359Chameleon 359

Chameleon 359 Normal

677 Phantom 677 Phantom 677 Normal

686 Phantom 686 Phantom 686 Normal

697 Phantom 697 Phantom 697 Normal

704 Dove 704 Dove 704 Normal

721 Dove 721 Dove 721 Normal

724Chameleon 724

Chameleon 724 Normal

726Chameleon 726

Chameleon 726 Normal

740 Phantom 740 Normal 740 Normal

741 Normal 741 Normal 741 Worm

743Chameleon 743 Normal 743

Chameleon

747 Phantom 747 Phantom 747 Normal

839 Normal 839 Phantom 839 Normal

EXAMPLE SUBJECT 839

SUBJECT 839 CORE GROUP

SUBJECT 839 + D2 TOP

SUBJECT 839 + D2 BOTTOM

SUBJECT 839 + D3 TOP

SUBJECT 839 + D3 BOTTOM

SUBJECT 839 + D2 + D3 TOP

SUBJECT 839 + D2 + D3 BOTTOM

•Low quality images tend to add subjects with low genuine scores and low imposter scores

•High quality images tend to add subjects with average to high genuine scores and low imposter scores

•Low quality additions also shifted the entire plot lower on the genuine axis

OBSERVATIONS

CONCLUSIONS

• Image quality does have some effect on the zoo characteristics of fingerprints

•Up to 10% of subjects changed zoo animals

•Even though the impostor scores have minimal change, subjects are still changing animal classifications

CONCLUSIONS

• Use larger and more variable populations such as top and bottom 25% together

• Are subjects stable when adding animal classifications from other datasets?

• Examine with fat plots instead of categorical analysis

• Can systems combine and still have the same results? – Interoperability of the zoo

FUTURE WORK

REFERENCES