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Human Identification Using Biometry


by Marco César Chaul, CBP. CIO/CEO Neokoros - Brasil

CBP – Certified Biometric Professional. Copyright © 2002 – 2013 Neokoros Brazil Ltd.


Fundamentals of Biometrics

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Biometry Definition Generic therm that describes a characteristic or a process

As a characteristic... ...refers to a biological or behavioral measure that can be used for automated recognition.

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Biometry Definition Generic therm that describes a characteristic or a process

As a process... ...relates to methods of automated recognition of the individual measures based on biological and behavioral characteristics Source: NSTC “Biometrics Glossary”

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Behavioral Traits Speak

Walk

Signature/Scribbling

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Behavioral Traits - Others Digital Fingerprint

Palm

Hand Geometry

Face

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Iris

Veins

DNA


Behavioral Traits - Others Thermograph y

Brain Waves

Cardiac Sounds

Odor

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Skin Texture


History 60’s - Physiology of speech production; - Semi-automatic face recognition; - Automatic fingerprint recognition; - Automatic signature recognition;

70’s - Behavioral components of speech; - Semi-automatic face recognition; - Automatic fingerprint recognition; - Automatic signature recognition;

80’s - Development of Continuous Assistance; - Iris recognition – concept; - Standards for the exchange of fingerprints; - Face recognition – new methods.

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History 90’s - Support of Government (Biometric Consortium – US); - Iris recognition; - Face recognition – FERET – Commercial Systems; - Palm print recognition; - Forensic DNA databank; - IAFIS – FBI (face & fingerprints)

2000 - Search methods (Vins, FRGC, iris patent); - Applications; - Rules and institutions support (Universities, ISO/IEC)

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Desirable Biometrics Characteristics - Universality; - Uniqueness; - Permanency; - Collectability; - Performance; - Acceptance; - Resistance to counterfeiting

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Biometric Recognition Patterns Fingerprints

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Topics - Identification X Verification; - Fingerprint;

- Image Capture; - Image Processing; - Fingerprint Differences; - Indexing;

- Usage; - Examples; - Neokoros Technology Differentials

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Identification – (1 to n) – (1:N) 1 – Subject provides biometric trait; 2 – The system searches on biometric database; 3 – The system identifies subject

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Verification – (1 to n) – (1:N) 1 – Subject provides previous identification; 2 – Subject provides biometric trait; 3 – The system checks biometric pattern.

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A F I S – Automated Fingerprint Identification System

Automated Fingerprint

Identification System

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Fingerprint - Formed by papillae of the skin (finger); - Unique and persistent;

- Papillae have ends and bifurcations; - Singularities: cores and deltas

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AFIS Stages

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AFIS Stages – Capture Image 3

- Several different technologies devices - FBI  500 DPI

- Divided in millions of pixels (picture elements);

- Each pixel is a sensor detecting the amount of incident light; - Result is a number matrix. Copyright © 2002 – 2013 Neokoros Brazil Ltd.


AFIS Stages – Capture Image

3

GAM 110

IMAGE CAPTURE

Fingerprint reader

Digital image (number matrix)

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AFIS Stages – Image Processing Apply numerical operations on pixel values procing a sharper image 122

101

85

122

65

32

152

125

98

213

125

67

210

160

118

255

220

118

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AFIS Stages – Extract Minutiae

- Find the minutiae (edges, bifurcations and singularities); - For each minutiae calculate papillae direction.

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AFIS Stages – Register Fingerprints - A set of minutia is represented as numbers and called a template.

- Images are not stored. Just the templates.

REGISTER TEMPLATE

BIOMETRIC DATABASE Copyright © 2002 – 2013 Neokoros Brazil Ltd.


AFIS Stages – Compare Minutiae

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AFIS Stages – Compare Fingerprints Differences in Positioning (and oclusion)

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AFIS Stages – Compare Fingerprints Rotation Differences

110º

140º

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AFIS Stages – Compare Fingerprints Very Dry Finger

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Search 1:N “False” - Browse sequentially the biometric database - Compare patterns one by one;

-Slow to be applied where the flow of people is great;

- Strategy is very slow for large databases (2000+ impressions);

- Several commercial systems with this standard.

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Search 1:N “Real” Neokoros Divided in two modules (algorithm). Module “N”: - Able to search and select MILLION samples in tenths of seconds; - Form geometric patterns with the extracted minutiae (triangles); - Perform a biometric search using indexing mechanism for quick search through patterns;

- The search returns a list of candidates with the highest level of similarity to the research sample.

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Search 1:N “Real” Neokoros Find matching geometric patterns

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Search 1:N “Real” Neokoros “K” Module - Detailed analysis of each candidate fingerprint to determine any matching with the researched fingerprint; - Consider the maximum possible rotation, the minutiae displacement

and other measurable parameters; - Calculate a comparison score; - Inform about the highest score fingerprint;

- Can use any algorithm based on a 1:1 minutiae - Currently we’re using Neurotechnologija and or Testech for verification. Both ranked two of four bests algorithms in the world

according 2004 FVC (Fingerprint Verification Competition 2004). Copyright © 2002 – 2013 Neokoros Brazil Ltd.


Search 1:N “Real” Neokoros Process/Show Results • When a fingerprint is found, fetch data associated with subject • This action is relevant to the kind of application. Examples:

- Show subject name - Activate door, turnstile, locks; - Register passage;

- Allow access to a software.

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Technology Practical Usage

- General public service

- Industries - Commercial enterprises;

- Services enterprises; - Commercial or residential buildings;

- Clubs and recreation centers; - Schools; - Ports; - Banks and financial institutions - Public safety;

- Airports - Universities;

- Theaters; - Thematic parks

-Hospitals and health centers - Credit card operators; - Food tickets enterprises… Copyright© 2002 – 2013 Neokoros Brazil Ltd.


Technology Practical Usage

...or any application requiring human recognition in real time.

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Differential -Clustering support;

- Supports mirroring; - Self learning;

- Proprietary file format with AES encryption; - Uses TCP/IP as native protocol to SDK; - Easy to use, based in events: (no biometric knowledge needed by users);

- Biometric system runs as service or application in Windows and Linux;

-Stable software (24/7 without interruptions);

- VEIA –Very Enhanced Images Algorithm improves fingerprint quality for children and elderly

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Neokoros X Neurotechnologija

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Thank You. Neokoros Brazil Ltd. Rua 86 N° 455 – Setor Sul Cep 74083-385 Goiânia-GO

55 62 3541-3230 www.neokoros.com info@neokoros.com

Copyright © 2002 – 2013 Neokoros Brazil Ltd.


Through Biometrics Human Identification