16-08-2011, 04:14 PM
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Fingerprint: Finger Biometrics
Fingerprint Identification
Among all the biometric techniques, fingerprint-based identification is the oldest method which has been successfully used in numerous applications.
Everyone is known to have unique, immutable fingerprints.
A fingerprint is made of a series of ridges and furrows on the surface of the finger.
The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points.
Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or a ridge ending.
Fingerprint Basics
A fingerprint has many identification and classification basics
Fingerprint Basics (minutiae)
Fingerprint Basics (minutiae)
Fingerprint Basics (minutiae)
Fingerprint Basics
How many different ridge characteristics can you see?
Fingerprint Identifications
A single rolled fingerprint may have as many as 100 or more identification points that can be used for identification purposes.
There is no exact size requirement as the number of points found on a fingerprint impression depend on the location of the print.
As an example the area immediately surrounding a delta will probably contain more points per square millimetre than the area near the tip of the finger which tends to not have that many points.
Fingerprint Representation
Fingerprinting was first created by Dr. Henry Fault, a British surgeon.
The general shape of the fingerprint is generally used to pre-process the images, and reduce the search in large databases.
These are:
Loop
Whorl
arch
There are several sub-categories of the above including:
right loop,
left loop,
Single or double whorl
Plain or tented arch
Ulnar or radial loops
The loop is by far the most common type of fingerprints.
The human population has fingerprints in the following percentages:
Loop – 65%
Whorl -- 30%
Arch -- 5%
Class Activity (15 minutes)
Classify the following fingerprints
Classify your right hand fingerprints
Check and classify your partner's right hand fingers.
Hand in your classification of your right hand finger – after being checked by your partner.
Fingerprint matching techniques
There are two categories of fingerprint matching techniques: minutae-based and correlation based.
Minutiae-based techniques first find minutiae points and then map their relative placement on the finger.
The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach.
Fingerprint Processing
Minutiae-based processing has problems including:
In real life you would have impressions made at separate times and subject to different pressure distortions.
On the average, many of these images are relatively clean and clear, however, in many of the actually crime scenes, prints are anything but clear.
There are cases where it is not easy to have a core pattern and a delta but only a latent that could be a fingertip, palm or even foot impression
The method does not take into account the global pattern of ridges and furrows.
Fingerprint matching based on minutiae has problems in matching different sized (unregistered) minutiae patterns.
Local ridge structures can not be completely characterized by minutiae.
The solution is to find an alternate representation of fingerprints which captures more local information and yields a fixed length code for the fingerprint.
Fingerprint Processing
Correlation-based processing has its own problems including:
Correlation-based techniques require the precise location of a registration point
It is also affected by image translation and rotation.
Fingerprint Processing
Human fingerprints are unique to each person and can be regarded as some sort of signature, certifying the person's identity.
Because straightforward matching between the fingerprint pattern to be identified and many already known patterns has problems due to its high sensitivity to errors (e.g. various noises, damaged fingerprint areas, or the finger being placed in different areas of fingerprint scanner window and with different orientation angles, finger deformation during the scanning procedure etc.).
Modern techniques focus on extracting minutiae points (points where capillary lines have branches or ends) from the fingerprint image, and check matching between the sets of fingerprint features.
A good reliable fingerprint processing technique requires sophisticated algorithms for reliable processing of the fingerprint image:
noise elimination,
minutiae extraction,
rotation and translation-tolerant fingerprint matching.
At the same time, the algorithms must be as fast as possible for comfortable use in applications with large number of users. It must also be able to fit into a microchip.
Progressive Fingerprint Matching
Image Processing
Capture the fingerprint images and process them through a series of image processing algorithms to obtain a clear unambiguous skeletal image of the original gray tone impression, clarifying smudged areas, removing extraneous artifacts and healing most scars, cuts and breaks.
Minutiae Extraction
Feature Detection for Matching Ridge ends and bifurcations (minutiae) within the skeletal image are identified and encoded, providing critical placement, orientation and linkage information for the fingerprint matching process.
Matching Fingerprint Search
The fingerprint matcher compares data from the input search print against all appropriate records in the database to determine if a probable match exists.
Minutia relationships, one to another are compared. Not as locations within an X-Y co-ordinate framework, but as linked relationships within a global context.
Each template comprises a multiplicity of information chunks, every information chunk representing a minutia and comprising a site, a minutia slant and a neighborhood.
Each site is represented by two coordinates. [ l = (x,y)]
The neighborhood comprises of positional parameters with respect to a chosen minutia for a predetermined figure of neighbor minutiae. In single embodiment, a neighborhood border is drown about the chosen minutia and neighbor minutiae are chosen from the enclosed region. [ theta]
A live template is compared to a stored measured template chunk-by-chunk. A chunk from the template is loaded in a random access memory (RAM).
The site, minutia slant and neighborhood of the reference information chunk are compared with the site, minutia slant and neighborhood of the stored template ( latent) information chunk by information chunk.
The neighborhoods are compared by comparing every positional argument. If every the positional parameters match, the neighbors match. If a predetermined figure of neighbor matches is met, the neighborhoods match.
If the matching rate of all information chunks is equivalent to or superior to the predetermined information chunk rate, the live template matches the stored (latent) template.
A selected fingerprint is mapped into a digital frame by a function f (minutiea type t, site l, neighborhood theta) =
f( t, l, theta).
Fingerprint Classification:
Large volumes of fingerprints are collected and stored everyday in a wide range of applications including forensics, access control, and driver license registration.
An automatic recognition of people based on fingerprints requires that the input fingerprint be matched with a large number of fingerprints in a database (FBI database contains approximately 70 million fingerprints!).
To reduce the search time and computational complexity, it is desirable to classify these fingerprints in an accurate and consistent manner so that the input fingerprint is required to be matched only with a subset of the fingerprints in the database.
Fingerprint Characteristics
Biometric (Fingerprint) Strengths
Finger tip most mature measure
Accepted reliability
High quality images
Small physical size
Low cost
Low False Acceptance Rate (FAR)
Small template (less than 500 bytes)
Biometric (Fingerprint weaknesses)
Requires careful enrollment
Potential high False Reject Rate (FRR) due to:
Pressing too hard, scarring, misalignment, dirt
Vendor incompatibility
Cultural issues
Physical contact requirement a negative in Japan
Perceived privacy issues with North America
Fingerprint Technology
As fingerprint technology matures, veriations in the technology also increase including:
Optical – finger is scanned on a platen ( glass, plastic or coasted glass/plastic).
Silicon – uses a silicon chip to read the capacitance value of the fingerprint. There are two types of this:
Active capacitance
Passive capacitance
Ultrasound – requires a large scanning device. It is appealing because it can better permeate dirt.
Class Activity
In groups of twos – discuss and write down the many uses of fingerprint technology.
Fingerprint: Finger Biometrics
Fingerprint Identification
Among all the biometric techniques, fingerprint-based identification is the oldest method which has been successfully used in numerous applications.
Everyone is known to have unique, immutable fingerprints.
A fingerprint is made of a series of ridges and furrows on the surface of the finger.
The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points.
Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or a ridge ending.
Fingerprint Basics
A fingerprint has many identification and classification basics
Fingerprint Basics (minutiae)
Fingerprint Basics (minutiae)
Fingerprint Basics (minutiae)
Fingerprint Basics
How many different ridge characteristics can you see?
Fingerprint Identifications
A single rolled fingerprint may have as many as 100 or more identification points that can be used for identification purposes.
There is no exact size requirement as the number of points found on a fingerprint impression depend on the location of the print.
As an example the area immediately surrounding a delta will probably contain more points per square millimetre than the area near the tip of the finger which tends to not have that many points.
Fingerprint Representation
Fingerprinting was first created by Dr. Henry Fault, a British surgeon.
The general shape of the fingerprint is generally used to pre-process the images, and reduce the search in large databases.
These are:
Loop
Whorl
arch
There are several sub-categories of the above including:
right loop,
left loop,
Single or double whorl
Plain or tented arch
Ulnar or radial loops
The loop is by far the most common type of fingerprints.
The human population has fingerprints in the following percentages:
Loop – 65%
Whorl -- 30%
Arch -- 5%
Class Activity (15 minutes)
Classify the following fingerprints
Classify your right hand fingerprints
Check and classify your partner's right hand fingers.
Hand in your classification of your right hand finger – after being checked by your partner.
Fingerprint matching techniques
There are two categories of fingerprint matching techniques: minutae-based and correlation based.
Minutiae-based techniques first find minutiae points and then map their relative placement on the finger.
The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach.
Fingerprint Processing
Minutiae-based processing has problems including:
In real life you would have impressions made at separate times and subject to different pressure distortions.
On the average, many of these images are relatively clean and clear, however, in many of the actually crime scenes, prints are anything but clear.
There are cases where it is not easy to have a core pattern and a delta but only a latent that could be a fingertip, palm or even foot impression
The method does not take into account the global pattern of ridges and furrows.
Fingerprint matching based on minutiae has problems in matching different sized (unregistered) minutiae patterns.
Local ridge structures can not be completely characterized by minutiae.
The solution is to find an alternate representation of fingerprints which captures more local information and yields a fixed length code for the fingerprint.
Fingerprint Processing
Correlation-based processing has its own problems including:
Correlation-based techniques require the precise location of a registration point
It is also affected by image translation and rotation.
Fingerprint Processing
Human fingerprints are unique to each person and can be regarded as some sort of signature, certifying the person's identity.
Because straightforward matching between the fingerprint pattern to be identified and many already known patterns has problems due to its high sensitivity to errors (e.g. various noises, damaged fingerprint areas, or the finger being placed in different areas of fingerprint scanner window and with different orientation angles, finger deformation during the scanning procedure etc.).
Modern techniques focus on extracting minutiae points (points where capillary lines have branches or ends) from the fingerprint image, and check matching between the sets of fingerprint features.
A good reliable fingerprint processing technique requires sophisticated algorithms for reliable processing of the fingerprint image:
noise elimination,
minutiae extraction,
rotation and translation-tolerant fingerprint matching.
At the same time, the algorithms must be as fast as possible for comfortable use in applications with large number of users. It must also be able to fit into a microchip.
Progressive Fingerprint Matching
Image Processing
Capture the fingerprint images and process them through a series of image processing algorithms to obtain a clear unambiguous skeletal image of the original gray tone impression, clarifying smudged areas, removing extraneous artifacts and healing most scars, cuts and breaks.
Minutiae Extraction
Feature Detection for Matching Ridge ends and bifurcations (minutiae) within the skeletal image are identified and encoded, providing critical placement, orientation and linkage information for the fingerprint matching process.
Matching Fingerprint Search
The fingerprint matcher compares data from the input search print against all appropriate records in the database to determine if a probable match exists.
Minutia relationships, one to another are compared. Not as locations within an X-Y co-ordinate framework, but as linked relationships within a global context.
Each template comprises a multiplicity of information chunks, every information chunk representing a minutia and comprising a site, a minutia slant and a neighborhood.
Each site is represented by two coordinates. [ l = (x,y)]
The neighborhood comprises of positional parameters with respect to a chosen minutia for a predetermined figure of neighbor minutiae. In single embodiment, a neighborhood border is drown about the chosen minutia and neighbor minutiae are chosen from the enclosed region. [ theta]
A live template is compared to a stored measured template chunk-by-chunk. A chunk from the template is loaded in a random access memory (RAM).
The site, minutia slant and neighborhood of the reference information chunk are compared with the site, minutia slant and neighborhood of the stored template ( latent) information chunk by information chunk.
The neighborhoods are compared by comparing every positional argument. If every the positional parameters match, the neighbors match. If a predetermined figure of neighbor matches is met, the neighborhoods match.
If the matching rate of all information chunks is equivalent to or superior to the predetermined information chunk rate, the live template matches the stored (latent) template.
A selected fingerprint is mapped into a digital frame by a function f (minutiea type t, site l, neighborhood theta) =
f( t, l, theta).
Fingerprint Classification:
Large volumes of fingerprints are collected and stored everyday in a wide range of applications including forensics, access control, and driver license registration.
An automatic recognition of people based on fingerprints requires that the input fingerprint be matched with a large number of fingerprints in a database (FBI database contains approximately 70 million fingerprints!).
To reduce the search time and computational complexity, it is desirable to classify these fingerprints in an accurate and consistent manner so that the input fingerprint is required to be matched only with a subset of the fingerprints in the database.
Fingerprint Characteristics
Biometric (Fingerprint) Strengths
Finger tip most mature measure
Accepted reliability
High quality images
Small physical size
Low cost
Low False Acceptance Rate (FAR)
Small template (less than 500 bytes)
Biometric (Fingerprint weaknesses)
Requires careful enrollment
Potential high False Reject Rate (FRR) due to:
Pressing too hard, scarring, misalignment, dirt
Vendor incompatibility
Cultural issues
Physical contact requirement a negative in Japan
Perceived privacy issues with North America
Fingerprint Technology
As fingerprint technology matures, veriations in the technology also increase including:
Optical – finger is scanned on a platen ( glass, plastic or coasted glass/plastic).
Silicon – uses a silicon chip to read the capacitance value of the fingerprint. There are two types of this:
Active capacitance
Passive capacitance
Ultrasound – requires a large scanning device. It is appealing because it can better permeate dirt.
Class Activity
In groups of twos – discuss and write down the many uses of fingerprint technology.
