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Fingerprint Identification

By: Travis R. Gault

Overview

IntroductionFeatures of interestFeature EncodingImplementations in IndustryConclusionsLab OverviewSteps for Classroom ImplementationReferences

IntroductionFeatures of interestFeature EncodingImplementations in IndustryConclusionsLab OverviewSteps for Classroom ImplementationReferences

Introduction

Fingerprints have been used for centuries for identification purposesMuch like irises, each fingerprint is unique to individualsEven identical twins have different fingerprints

Fingerprints have been used for centuries for identification purposesMuch like irises, each fingerprint is unique to individualsEven identical twins have different fingerprints

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Introduction

There are three fundamental principles of fingerprints [3].1. A fingerprint is an individual characteristic and no

two fingers have identical ridge characteristics2. A fingerprint will remain unchanged during an

individuals lifetime.3. Fingerprints have general ridge patterns, making

it possible to systematically classify them.

There are three fundamental principles of fingerprints [3].1. A fingerprint is an individual characteristic and no

two fingers have identical ridge characteristics2. A fingerprint will remain unchanged during an

individuals lifetime.3. Fingerprints have general ridge patterns, making

it possible to systematically classify them.

Features of Interest

Global Features - are the characteristics that any human can see with the naked eye

Basic Ridge PatternsPattern AreaDeltaType LinesRidge Count

Local Features - or Minutia Points are the unique characteristics of fingerprint ridges that are used for positive identification.Its possible to have the same global features, but the local features remain unique.

Global Features - are the characteristics that any human can see with the naked eye

Basic Ridge PatternsPattern AreaDeltaType LinesRidge Count

Local Features - or Minutia Points are the unique characteristics of fingerprint ridges that are used for positive identification.Its possible to have the same global features, but the local features remain unique.

Features of Interest

Pattern Area - is the part of the fingerprint that contains all the global features. However, some local features may be found outside the pattern area.

Images taken from [1].

Pattern Area - is the part of the fingerprint that contains all the global features. However, some local features may be found outside the pattern area.

Images taken from [1].

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Features of Interest

Core Point - is the approximate center of the fingerprint, and is used as the reference point for reading/classifying the print.More specifically it is defined as the topmost point on the innermost upwardly curving ridgelineThe figure below, taken from [2], shows some fingerprints and their core point locations

Core Point - is the approximate center of the fingerprint, and is used as the reference point for reading/classifying the print.More specifically it is defined as the topmost point on the innermost upwardly curving ridgelineThe figure below, taken from [2], shows some fingerprints and their core point locations

Features of Interest

Type Lines - are the two innermost ridges that start parallel, diverge, and tend to surround the pattern area.

Type Lines - are the two innermost ridges that start parallel, diverge, and tend to surround the pattern area.

Type Lines

Features of Interest

Delta - is the point on the first bifurcation, abrupt ending ridge, meeting of two ridges, dot, fragmentary ridge, or any point on a ridge at or nearest the center of divergence of two type lines, located at or directly in front of their point of divergence.

Delta - is the point on the first bifurcation, abrupt ending ridge, meeting of two ridges, dot, fragmentary ridge, or any point on a ridge at or nearest the center of divergence of two type lines, located at or directly in front of their point of divergence.

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Features of Interest

Ridge Count - is the number of ridges between the delta and the core. This is done by drawing an imaginary line from the Delta to the Core and each ridge that touches this line is counted.

Ridge Count - is the number of ridges between the delta and the core. This is done by drawing an imaginary line from the Delta to the Core and each ridge that touches this line is counted.

Features of Interest

In the figure below, taken from [4], the circle is the core, the triangle is the delta, the square is a minutiae-ridge ending and the diamond is a minutiae-ridge bifurcation. The orange line is the ridge count line.

In the figure below, taken from [4], the circle is the core, the triangle is the delta, the square is a minutiae-ridge ending and the diamond is a minutiae-ridge bifurcation. The orange line is the ridge count line.

Features of Interest

Local FeaturesRidge Ending - ridge endsRidge Bifurcation - divides into branchesRidge Divergence - diverging of two parallel linesDot or Island - small ridgeEnclosure - divides and reunitesShort Ridge - like a dot/island, but larger

Image taken from [5].

Local FeaturesRidge Ending - ridge endsRidge Bifurcation - divides into branchesRidge Divergence - diverging of two parallel linesDot or Island - small ridgeEnclosure - divides and reunitesShort Ridge - like a dot/island, but larger

Image taken from [5].

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Features of Interest

Basic Ridge PatternsLoop - is the most common (~65% of all prints)Arch - more open curve than a loopWhorl - ridge that makes a complete circle (~30% of all prints)

Basic Ridge PatternsLoop - is the most common (~65% of all prints)Arch - more open curve than a loopWhorl - ridge that makes a complete circle (~30% of all prints)

Feature Encoding

Manual Human experts use a combination of visual, textural, minutiae cues and experience for verificationStill used in the final stages of law enforcement applications

Image basedUtilizes only visual appearance. Requires the complete image to be stored (large template sizes)

Taken from [5].

Manual Human experts use a combination of visual, textural, minutiae cues and experience for verificationStill used in the final stages of law enforcement applications

Image basedUtilizes only visual appearance. Requires the complete image to be stored (large template sizes)

Taken from [5].

Feature Encoding

Texture basedTreats the fingerprint as an oriented texture imageLess accurate than minutiae based matchers since most regions in the fingerprints carry low textural content

Minutiae basedUses the relative position of the minutiae pointsThe most popular and accurate approach for verificationResembles manual approach very closely.From a software perspective, the minutia are also used to align the images for database comparisons.

Texture basedTreats the fingerprint as an oriented texture imageLess accurate than minutiae based matchers since most regions in the fingerprints carry low textural content

Minutiae basedUses the relative position of the minutiae pointsThe most popular and accurate approach for verificationResembles manual approach very closely.From a software perspective, the minutia are also used to align the images for database comparisons.

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Feature Encoding

Image Based Matching Using Optical CorrelationAdvantages

Image itself is used as the templateRequires only low resolution imagesFast

DisadvantagesImage itself is used as the templateRequires accurate alignment of the two printsNot robust to changes in scale, orientation and position

Taken from [5]

Image Based Matching Using Optical CorrelationAdvantages

Image itself is used as the templateRequires only low resolution imagesFast

DisadvantagesImage itself is used as the templateRequires accurate alignment of the two printsNot robust to changes in scale, orientation and position

Taken from [5]

Feature Encoding

Texture Based MatchingAdvantages

Uses texture information (lost in optical and minutiae based schemes)Performs well with poor quality printsFeatures are statistically independent from minutiae and can be combined with minutiae matchers for higher accuracy

DisadvantagesRequires accurate alignment of the two printsVaries with translation, orientation and non-linear distortionLess accurate than minutiae based matchers

Taken from [5]

Texture Based MatchingAdvantages

Uses texture information (lost in optical and minutiae based schemes)Performs well with poor quality printsFeatures are statistically independent from minutiae and can be combined with minutiae matchers for higher accuracy

DisadvantagesRequires accurate alignment of the two printsVaries with translation, orientation and non-linear distortionLess accurate than minutiae based matchers

Taken from [5]

Feature Encoding

Minutiae Based MatchingAdvantages

Invariant to translation, rotation and scale changesVery accurate

DisadvantagesError prone in low quality imagesNot robust to non-linear distortionDoes not use visual and textural cuesTaken from [5].

Minutiae Based MatchingAdvantages

Invariant to translation, rotation and scale changesVery accurate

DisadvantagesError prone in low quality imagesNot robust to non-linear distortionDoes not use visual and textural cuesTaken from [5].

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Feature Encoding

In simple terms, were looking for areas where ridges stop or intersect another ridge.This data is stored and used for comparisons to other templatesImage taken from [5].

In simple terms, were looking for areas where ridges stop or intersect another ridge.This data is stored and used for comparisons to other templatesImage taken from [5].

Implementations

Binarization ApproachUsed by:

MINDTCT fingerprint minutiae detection systemNIST uses this in a lot of implementationsNBIS - NIST Biometric Image Software

Done directionally or with template matching to detect minutiaeAdaptive Flow Orientation Technique

Binarization is done by peak detectionBut this leads to false positive in areas of poor contrast

Direct Gray Scale Ridge Following Based on ridge pursuitHas low computational complexityCannot handle poor contrast prints and images with poor ridge structure Relies on a good orientation map for ridge pursuit

Modified from [5].

Binarization ApproachUsed by:

MINDTCT fingerprint minutiae detection systemNIST uses this in a lot of implementationsNBIS - NIST Biometric Image Software

Done directionally or with template matching to detect minutiaeAdaptive Flow Orientation Technique

Binarization is done by peak detectionBut this leads to false positive in areas of poor contrast

Direct Gray Scale Ridge Following Based on ridge pursuitHas low computational complexityCannot handle poor contrast prints and images with poor ridge structure Relies on a good orientation map for ridge pursuit

Modified from [5].

Implementations

Images taken from [5].Images taken from [5].

Binarization Thinning Minutia DetectionAcquisition

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Implementations

The biometrics lab setup uses the Binarization approach.Additionally, a variety of software is freely available on the web for C++, C#, Java, and VB implementations.

The biometrics lab setup uses the Binarization approach.Additionally, a variety of software is freely available on the web for C++, C#, Java, and VB implementations.

Implementations

How successful is Fingerprint Matching?Typical False Rejection Rates range between 0.03%-1.4% [1,6]Typical False Acceptance Rates are around 0.01%-0.001% [1,6].These rates depend on the manufacturer and the algorithm usedComparatively, iris FRR is about 0.001% and FAR is about 1 in billions (basically 0).

How successful is Fingerprint Matching?Typical False Rejection Rates range between 0.03%-1.4% [1,6]Typical False Acceptance Rates are around 0.01%-0.001% [1,6].These rates depend on the manufacturer and the algorithm usedComparatively, iris FRR is about 0.001% and FAR is about 1 in billions (basically 0).

Conclusions

Fingerprinting is still a viable means for biometric identification, especially in law enforcement, where fingerprints may be left behind.Fingerprints also offer a cheaper solution for day-to-day activities than iris recognitionFingerprints are not as good for high-security implementationMore work needs to be done to develop better methods to compensate for the variations in fingerprint recording when identifying local features

Fingerprinting is still a viable means for biometric identification, especially in law enforcement, where fingerprints may be left behind.Fingerprints also offer a cheaper solution for day-to-day activities than iris recognitionFingerprints are not as good for high-security implementationMore work needs to be done to develop better methods to compensate for the variations in fingerprint recording when identifying local features

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Lab Overview

Each classmate will submit their right thumbprint twice

One set of thumbprints will serve as a galleryOne set of thumbprints will serve as a test gallery, along with two additional random prints

Students will use MATLAB and code examples provided to encode fingerprint minutiaeStudents will test each print from the test gallery and shall report on the FAR and FRR.Students must also include technical documentation as well as a small research segment into the field of fingerprint analysis.

Each classmate will submit their right thumbprint twice

One set of thumbprints will serve as a galleryOne set of thumbprints will serve as a test gallery, along with two additional random prints

Students will use MATLAB and code examples provided to encode fingerprint minutiaeStudents will test each print from the test gallery and shall report on the FAR and FRR.Students must also include technical documentation as well as a small research segment into the field of fingerprint analysis.

Lab Overview

This lab will be officially distributed on Tues., Nov. 12, and will be due no later than Noon in the CVIP Lab on Dec. 8.Students must submit all code and supporting files with the report.Students must arrange to demo their work.

This lab will be officially distributed on Tues., Nov. 12, and will be due no later than Noon in the CVIP Lab on Dec. 8.Students must submit all code and supporting files with the report.Students must arrange to demo their work.

Steps for Implemetation

6 Steps:PreprocessingBinarizationThinningMinutiae identificationMinutiae alignmentDatabase comparisons

6 Steps:PreprocessingBinarizationThinningMinutiae identificationMinutiae alignmentDatabase comparisons

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Preprocessing

Unless you have a very high quality fingerprint reader, youll need to enhance the image after croppingThere are two goals:

Increase the contrast in the imageBridge empty spots in ridges

TechniquesHistogram equalization (histeq)Gamma correctionGabor Filter (gaborfilter)

Download from: http://www.mathworks.com/matlabcentral/fileexchange/5237

Unless you have a very high quality fingerprint reader, youll need to enhance the image after croppingThere are two goals:

Increase the contrast in the imageBridge empty spots in ridges

TechniquesHistogram equalization (histeq)Gamma correctionGabor Filter (gaborfilter)

Download from: http://www.mathworks.com/matlabcentral/fileexchange/5237

Histogram Equalization

The histogram equalization technique is used to improve global image contrast.However, the disadvantage is that it may have the incorrect effect by increasing background noise while decreasing the signal of interest.The implementation of this will depend on the images rendered by your input device.

The histogram equalization technique is used to improve global image contrast.However, the disadvantage is that it may have the incorrect effect by increasing background noise while decreasing the signal of interest.The implementation of this will depend on the images rendered by your input device.

Gabor Filter

The Gabor filter is basically a Gaussian (with variances Sx and Sy along x and y-axes respectively) modulated by a complex sinusoid (with center frequencies U and V along x and y-axes respectively).[9]This will help bridge the empty spots along ridges.

The Gabor filter is basically a Gaussian (with variances Sx and Sy along x and y-axes respectively) modulated by a complex sinusoid (with center frequencies U and V along x and y-axes respectively).[9]This will help bridge the empty spots along ridges.

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Binarization

Next, convert the 8-bit grayscale images to 1-bit images

Select a window block size (say 32x32)Find the average of the values in the blockSet values greater than the average equal 1, else 0

Next, convert the 8-bit grayscale images to 1-bit images

Select a window block size (say 32x32)Find the average of the values in the blockSet values greater than the average equal 1, else 0

Thinning

Next, we must convert the binary image to a set of lines. This process is called thinning.MATLABs morphological toolbox facilitates thisbwmorph( bin_img, thin, inf)

Next, we must convert the binary image to a set of lines. This process is called thinning.MATLABs morphological toolbox facilitates thisbwmorph( bin_img, thin, inf)

Minutiae Detection

We are primarily interested in two types of minutia

Termination Points where a ridge endsBifurcation Points where a ridge splits

Now, consider a 3x3 window in the thinned image (see right). We are interested in figures that look like the top row or rotated versions.We are not interested in images like the bottom row

We are primarily interested in two types of minutia

Termination Points where a ridge endsBifurcation Points where a ridge splits

Now, consider a 3x3 window in the thinned image (see right). We are interested in figures that look like the top row or rotated versions.We are not interested in images like the bottom row

101010010

Bifurcation

000010010

Termination

001001010

Not Interested

001000101

Not Interested

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Minutiae Detection

How do you tell the difference between valid and invalid windows?

Windows with the center value set equal to 0 are of no interest to us.

How do you tell the difference between valid and invalid windows?

Windows with the center value set equal to 0 are of no interest to us.

001001010

Not Interested

001000101

Not Interested

Minutiae Detection

How do you distinguish between bifurcation and termination points?

Sum the entries and subtract 1.If the answer is 3, then its a bifurcation point,If the answer is 1, then its a termination point

How do you distinguish between bifurcation and termination points?

Sum the entries and subtract 1.If the answer is 3, then its a bifurcation point,If the answer is 1, then its a termination point

101010010

Bifurcation

000010010

Termination

001010010

Not Interested

000010000

Not Interested

Minutiae Detection

This is an example of detecting the termination points from a thinned image taken with the labs finger print reader.Done using MATLAB

This is an example of detecting the termination points from a thinned image taken with the labs finger print reader.Done using MATLAB

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Minutiae Detection

This is an example of detecting the bifurcation points from a thinned image taken with the labs finger print reader.Done using MATLAB

This is an example of detecting the bifurcation points from a thinned image taken with the labs finger print reader.Done using MATLAB

Minutiae Detection

This is a composite of both the bifurcation and termination points along with the original thinned image.

This is a composite of both the bifurcation and termination points along with the original thinned image.

Minutiae Detection

After identifying the minutiae as on the previous slides, there are a lot of spurious minutiae that need to be removedThis can be done removing minutiae that occur with an arbitrary Euclidian distance from one another.We need to check the following points distances:

Termination-TerminationTermination-Bifurcation (and vice versa)Bifurcation-Bifurcation

Coordinates separated by a smaller distance than specified shall be removed

After identifying the minutiae as on the previous slides, there are a lot of spurious minutiae that need to be removedThis can be done removing minutiae that occur with an arbitrary Euclidian distance from one another.We need to check the following points distances:

Termination-TerminationTermination-Bifurcation (and vice versa)Bifurcation-Bifurcation

Coordinates separated by a smaller distance than specified shall be removed

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Minutiae Detection

Next Time

On Tuesday:ROI selectionMinutiae Orientation/AlignmentGallery comparisonFingerprint project distributedFingerprint acquisition (CVIP Lab)

On Tuesday:ROI selectionMinutiae Orientation/AlignmentGallery comparisonFingerprint project distributedFingerprint acquisition (CVIP Lab)

ROI Selection

The ROI selection is determined using MATLABs morpho-logical toolbox by running the thinned image is through imclose(), imerode() and imfill().The results are shown below.

The ROI selection is determined using MATLABs morpho-logical toolbox by running the thinned image is through imclose(), imerode() and imfill().The results are shown below.

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ROI Selection

The next step is to mask out coordinates outside the ROI. Graphically, the results are shown below.The next step is to mask out coordinates outside the ROI. Graphically, the results are shown below.

Termination Orientation

Consider a 5x5 square centered about a termination point.We know the angle of each border point w.r.t. the center point.When the termination point intersects the border, we then know a rough estimate of the angle.

Consider a 5x5 square centered about a termination point.We know the angle of each border point w.r.t. the center point.When the termination point intersects the border, we then know a rough estimate of the angle.

-135

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135

-45-60-90-120

-30

0X

30

456090120

Termination Orientation

ExampleLet the red blocks represent a termination ridgeFrom this, we determine the angle to be 120 degrees

ExampleLet the red blocks represent a termination ridgeFrom this, we determine the angle to be 120 degrees

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Termination Orientation

ExampleLet the red blocks represent a termination ridgeFrom this, we have two possible angles: 120 or 135 degrees.We must choose which to use.In my code, I use the angle representing the smallest distance from the center. In this case 135.

ExampleLet the red blocks represent a termination ridgeFrom this, we have two possible angles: 120 or 135 degrees.We must choose which to use.In my code, I use the angle representing the smallest distance from the center. In this case 135.

-135

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Termination Orientation

ExampleLet the red blocks represent a termination ridgeFrom this, we have no possible angles, which indicates noise.In this case, we remove the point from the list of termination minutia.

ExampleLet the red blocks represent a termination ridgeFrom this, we have no possible angles, which indicates noise.In this case, we remove the point from the list of termination minutia. -135

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Bifurcation Orientation

ExampleLet the red blocks represent a bifurcation ridge and an unknown ridge.We must determine all three angles, and exclude the angles from the unknown ridge.To do this, apply bwlabel(). One region will be labeled with 1s, the other with 2s.Select the correct region that by choosing the label assigned to X

ExampleLet the red blocks represent a bifurcation ridge and an unknown ridge.We must determine all three angles, and exclude the angles from the unknown ridge.To do this, apply bwlabel(). One region will be labeled with 1s, the other with 2s.Select the correct region that by choosing the label assigned to X

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Bifurcation Orientation

ExampleRemove X and apply bwlabel() again. In the second figure, red = 1, green = 2, blue = 3Perform a similar operation to determine the three angles by analyzing each label (1,2,3).Handle multiple angles for each label as with termination points or similarly

ExampleRemove X and apply bwlabel() again. In the second figure, red = 1, green = 2, blue = 3Perform a similar operation to determine the three angles by analyzing each label (1,2,3).Handle multiple angles for each label as with termination points or similarly

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Orientation Results

The results of finding the termination and bifurcation points is on the right.

The results of finding the termination and bifurcation points is on the right.

Gallery Comparisons

General Steps1. Select any one minutia from image A and B. This will serve as the origin in each image.2. Use the orientation and location information from the minutia in image A to transform all minutia in image B, about its origin.3. Perform a similarity comparison between the minutia in A to the transformed minutia in B.

There are a large variety of algorithms and types availableYou will investigate and select algorithm to use in the project

4. Repeat for all minutia in B.5. Select a new image A to compare against B if the similarity score is insufficient.

General Steps1. Select any one minutia from image A and B. This will serve as the origin in each image.2. Use the orientation and location information from the minutia in image A to transform all minutia in image B, about its origin.3. Perform a similarity comparison between the minutia in A to the transformed minutia in B.

There are a large variety of algorithms and types availableYou will investigate and select algorithm to use in the project

4. Repeat for all minutia in B.5. Select a new image A to compare against B if the similarity score is insufficient.

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Gallery Comparisons

Graphically, this is what we are doing w.r.t. rotating the coordinates.Image taken from [8].

Graphically, this is what we are doing w.r.t. rotating the coordinates.Image taken from [8].

D

E FD

EF

X-axis

Y-axis

X'-axis

Y'-axisy

x

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EF

D

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D

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References

1. DigitalPersonaTM White Paper: Guide to Fingerprint Identification. Voice Recognition Systems. www.talktoyourcomputer.com

2. Frequently Asked Questions. SecuGen Biometrics Solutions. http://www.secugen.com/support/faqs.htm

3. Fingerprint Techniques Manual. New Mexico Department of Health, Division of Health Improvement.

4. Liu, L.; Dai, T.; Hsin-Chu. Ridge Orientation Estimation and Verification Algorithm for Fingerprint Enhancement. Journal of Universal Knowledge Management. Vol. 12, Issue 10. Pgs. 1426-1438.

5. Chikkerur, S. Online Fingerprint Verification. Center for Unified Biometrics and Sensors. University at Buffalo.

6. Security Issues. Affirmative Technologies. http://www.affirmativeusa.com/security_issues.htm

7. FingerPrint Demo. MathWorks. http://www.mathworks.com/matlabcentral/fx_files/16728/1/content/FingerPrint/html/fingerprint.html

1. DigitalPersonaTM White Paper: Guide to Fingerprint Identification. Voice Recognition Systems. www.talktoyourcomputer.com

2. Frequently Asked Questions. SecuGen Biometrics Solutions. http://www.secugen.com/support/faqs.htm

3. Fingerprint Techniques Manual. New Mexico Department of Health, Division of Health Improvement.

4. Liu, L.; Dai, T.; Hsin-Chu. Ridge Orientation Estimation and Verification Algorithm for Fingerprint Enhancement. Journal of Universal Knowledge Management. Vol. 12, Issue 10. Pgs. 1426-1438.

5. Chikkerur, S. Online Fingerprint Verification. Center for Unified Biometrics and Sensors. University at Buffalo.

6. Security Issues. Affirmative Technologies. http://www.affirmativeusa.com/security_issues.htm

7. FingerPrint Demo. MathWorks. http://www.mathworks.com/matlabcentral/fx_files/16728/1/content/FingerPrint/html/fingerprint.html

References

8. Zhili, W. Fingerprint Recognition. Department of Computer Science. BS Thesis. Hong Kong Baptist University. http://www.comp.hkbu.edu.hk/~vincent/resPaper.htm

9. 2D Gabor Filter (Ver 1,2,3). MathWorks. http://www.mathworks.com/matlabcentral/fileexchange/5237

10. Kenner, F. FingerPrint Demo. Mathworks. http://www.mathworks.com/matlabcentral/fx_files/16728/1/content/FingerPrint/html/fingerprint.html

8. Zhili, W. Fingerprint Recognition. Department of Computer Science. BS Thesis. Hong Kong Baptist University. http://www.comp.hkbu.edu.hk/~vincent/resPaper.htm

9. 2D Gabor Filter (Ver 1,2,3). MathWorks. http://www.mathworks.com/matlabcentral/fileexchange/5237

10. Kenner, F. FingerPrint Demo. Mathworks. http://www.mathworks.com/matlabcentral/fx_files/16728/1/content/FingerPrint/html/fingerprint.html