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Fingerprint Matching Fingerprint Matching

TechnologyTechnology

The BasicsThe Basics

Fingerprint Matching Fingerprint Matching

TechnologyTechnology

The BasicsThe Basics

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OverviewOverview

A wide variety of fingerprint matching software and A wide variety of fingerprint matching software and hardware is availablehardware is available

AuthenTec sensors can work with most varieties of AuthenTec sensors can work with most varieties of matching systems including:matching systems including: AuthenTec supplied matchersAuthenTec supplied matchers Most independently available matchersMost independently available matchers

Fingerprint matchers are catagorized:Fingerprint matchers are catagorized: Primarily - by type of data usedPrimarily - by type of data used Secondarily – by method of comparing that dataSecondarily – by method of comparing that data

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Ridge PatternsRidge Patterns Macro-featuresMacro-features

– Core, deltas, scarsCore, deltas, scars

Classical Ridge MinutiaClassical Ridge Minutia

Generalized PatternGeneralized Pattern Specific ridge patternSpecific ridge pattern

Fine StructureFine Structure Ridge shapeRidge shape

– Lateral ridge shapeLateral ridge shape– Vertical ridge shapeVertical ridge shape

Local curvatureLocal curvature Pores (sweat glands)Pores (sweat glands)

What data is in a Fingerprint Image?What data is in a Fingerprint Image?

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What data is in a Fingerprint Image?What data is in a Fingerprint Image?Ridge PatternsRidge Patterns Macro-featuresMacro-features

– Core, deltas, scarsCore, deltas, scars

Classical Ridge MinutiaClassical Ridge Minutia

Generalized PatternGeneralized Pattern Specific ridge patternSpecific ridge pattern

Fine StructureFine Structure Ridge shapeRidge shape

– Lateral ridge shapeLateral ridge shape– Vertical ridge shapeVertical ridge shape

Local curvatureLocal curvature Pores (sweat glands)Pores (sweat glands)

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What data is in a Fingerprint Image?What data is in a Fingerprint Image?Ridge PatternsRidge Patterns Macro-featuresMacro-features

– Core, deltas, scarsCore, deltas, scars

Classical Ridge MinutiaClassical Ridge Minutia

Generalized PatternGeneralized Pattern Specific ridge patternSpecific ridge pattern

Fine StructureFine Structure Ridge shapeRidge shape

– Lateral ridge shapeLateral ridge shape– Vertical ridge shapeVertical ridge shape

Local curvatureLocal curvature Pores (sweat glands)Pores (sweat glands)

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What data is in a Fingerprint Image?What data is in a Fingerprint Image?Ridge PatternsRidge Patterns Macro-featuresMacro-features

– Core, deltas, scarsCore, deltas, scars

Classical Ridge MinutiaClassical Ridge Minutia Generalized PatternGeneralized Pattern Specific ridge patternSpecific ridge pattern

Fine StructureFine Structure Ridge shapeRidge shape

– Lateral ridge shapeLateral ridge shape– Vertical ridge shapeVertical ridge shape

Local curvatureLocal curvature Pores (sweat glands)Pores (sweat glands)

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How is this data best used?How is this data best used?

Data classData class Typical usageTypical usage LimitationsLimitations

Macro-featuresMacro-featuresCore & deltasCore & deltas

Sample alignmentSample alignment

Large DB indexing (inter-Large DB indexing (inter-feature ridge counts)feature ridge counts)

Best used with rolled finger imagesBest used with rolled finger images

Deltas often out-of-frame in simple Deltas often out-of-frame in simple touch imagestouch images

Classical ridge minutiaClassical ridge minutia Ridge endingsRidge endings Bifurcations Bifurcations

Efficient one-to-many Efficient one-to-many matchingmatching

Human-in-the-loop Human-in-the-loop matching (e.g., FBI)matching (e.g., FBI)

Sometimes error prone in cracked Sometimes error prone in cracked elderly fingerselderly fingers

Small sensor images have too few Small sensor images have too few minutia.minutia.

Generalized patternGeneralized pattern Cell matricesCell matrices

Efficient one-to-one and Efficient one-to-one and one-to-few matchingone-to-few matching

Small sensor matchingSmall sensor matching

Emerging tech less understoodEmerging tech less understood

Template size grows in one-to-many Template size grows in one-to-many applicationsapplications

Specific patternSpecific pattern Full patternFull pattern Hot spotsHot spots

One-to-one matching with One-to-one matching with low FARlow FAR

Custom hardware assisted Custom hardware assisted matchingmatching

Computation intensiveComputation intensive

May have larger template sizeMay have larger template size

Fine structureFine structure Ridge width patnRidge width patn PoresPores

Small sensor matchingSmall sensor matching

Partial image latent printsPartial image latent prints

Requires higher quality & more Requires higher quality & more repeatable imagesrepeatable images

Computation intensiveComputation intensive

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Commercial Fingerprint Matcher TrendsCommercial Fingerprint Matcher Trends

Take advantage of the higher powered processors and Take advantage of the higher powered processors and higher quality images to match with small-area sensorshigher quality images to match with small-area sensors Utilize smaller & denser featuresUtilize smaller & denser features

– That are now sufficiently repeatable (in images from the best That are now sufficiently repeatable (in images from the best quality sensors) for use in matchingquality sensors) for use in matching

– to achieve accurate matching with small-area sensorsto achieve accurate matching with small-area sensors Multiple view compositing (mosaic building)Multiple view compositing (mosaic building)

– Permits flexible finger positioning even on very small Permits flexible finger positioning even on very small sensorssensors

Optimized for 1 to few matchingOptimized for 1 to few matching For personal computing and communication devicesFor personal computing and communication devices Eliminates the complex structures used for large Eliminates the complex structures used for large

database searching and indexingdatabase searching and indexing

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For more information …For more information …

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