MatchingBasics12.pdf

Fingerprint Matching
Technology
The Basics
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Overview
A wide variety of fingerprint matching software and
hardware is available
AuthenTec sensors can work with most varieties of
matching systems including:
z AuthenTec supplied matchers
z Most independently available matchers
Fingerprint matchers are catagorized:
z Primarily - by type of data used
z Secondarily – by method of comparing that data
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What data is in a Fingerprint Image?
Ridge Patterns
z Macro-features
– Core, deltas, scars
z
Classical Ridge Minutia
z
Generalized Pattern
z
Specific ridge pattern
Fine Structure
z Ridge shape
– Lateral ridge shape
– Vertical ridge shape
z
z
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Local curvature
Pores (sweat glands)
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techieDetail16.ppt
What data is in a Fingerprint Image?
Ridge Patterns
z Macro-features
– Core, deltas, scars
z
Classical Ridge Minutia
z
Generalized Pattern
z
Specific ridge pattern
Fine Structure
z Ridge shape
– Lateral ridge shape
– Vertical ridge shape
z
z
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Local curvature
Pores (sweat glands)
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techieDetail16.ppt
What data is in a Fingerprint Image?
Ridge Patterns
z Macro-features
– Core, deltas, scars
z
Classical Ridge Minutia
z
Generalized Pattern
z
Specific ridge pattern
Fine Structure
z Ridge shape
– Lateral ridge shape
– Vertical ridge shape
z
z
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Local curvature
Pores (sweat glands)
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techieDetail16.ppt
What data is in a Fingerprint Image?
Ridge Patterns
z Macro-features
– Core, deltas, scars
z
z
z
Classical Ridge Minutia
Generalized Pattern
Specific ridge pattern
Fine Structure
z Ridge shape
– Lateral ridge shape
– Vertical ridge shape
z
z
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Local curvature
Pores (sweat glands)
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techieDetail16.ppt
How is this data best used?
Data class
Macro-features
Core & deltas
Classical ridge minutia
z Ridge endings
z Bifurcations
Generalized pattern
z Cell matrices
Specific pattern
z Full pattern
z Hot spots
Fine structure
z Ridge width patn
z Pores
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Typical usage
Limitations
Sample alignment
Large DB indexing (interfeature ridge counts)
Efficient one-to-many
matching
Human-in-the-loop
matching (e.g., FBI)
Efficient one-to-one and
one-to-few matching
Best used with rolled finger
images
Deltas often out-of-frame in
simple touch images
Sometimes error prone in
cracked elderly fingers
Small sensor images have too
few minutia.
Emerging tech less understood
Template size grows in one-tomany applications
Small sensor matching
One-to-one matching with
Computation intensive
low FAR
May have larger template size
Custom hardware
assisted matching
Small sensor matching
Requires higher quality & more
repeatable images
Partial image latent prints
Computation intensive
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Commercial Fingerprint Matcher Trends
Take advantage of the higher powered processors and
higher quality images to match with small-area sensors
z Utilize smaller & denser features
–
That are now sufficiently repeatable (in images from the best
quality sensors) for use in matching
– to achieve accurate matching with small-area sensors
z
Multiple view compositing (mosaic building)
– Permits flexible finger positioning even on very small
sensors
Optimized for 1 to few matching
z For personal computing and communication devices
z Eliminates the complex structures used for large
database searching and indexing
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For more information …
Click here to learn
how very small
fingerprint
sensors work
Click here to learn
about Specifying
commercial
biometric systems
Back to Beginning
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