Human Identification Using Biometry

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

Fundamentals of Biometrics

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.

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

Behavioral Traits Speak Walk Signature/Scribbling

Behavioral Traits - Others Digital Fingerprint Palm Hand Geometry Veins Face Iris DNA

Behavioral Traits - Others Thermograph y Brain Waves Cardiac Sounds Odor 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.

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)

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

Biometric Recognition Patterns Fingerprints

Topics - Identification X Verification; - Fingerprint; - Image Capture; - Image Processing; - Fingerprint Differences; - Indexing; - Usage; - Examples; - Neokoros Technology Differentials

A F I S Automated Fingerprint Identification System Automated Fingerprint Identification System

Fingerprint - Formed by papillae of the skin (finger); - Unique and persistent; - Papillae have ends and bifurcations; - Singularities: cores and deltas

AFIS Stages

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.

AFIS Stages Capture Image 3 GAM 110 IMAGE CAPTURE Fingerprint reader Digital image (number matrix)

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

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

AFIS Stages Register Fingerprints - A set of minutia is represented as numbers and called a template. - Images are not stored. Just the templates. TEMPLATE REGISTER BIOMETRIC DATABASE

AFIS Stages Compare Minutiae

AFIS Stages Compare Fingerprints Differences in Positioning (and oclusion)

AFIS Stages Compare Fingerprints Rotation Differences 110º 140º

AFIS Stages Compare Fingerprints Very Dry Finger

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.

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. Copyright 2002 2013 Neokoros Brazil Ltd

Search 1:N Real Neokoros Find matching geometric patterns

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).

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. Copyright 2002 2013 Neokoros Brazil Ltda.

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.

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

Neokoros X Neurotechnologija

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