by amit mhatre and roshith rajagopal fingerprint topics covered sensors used sensors used...

by Amit Mhatre and Roshith Rajagoby Amit Mhatre and Roshith Rajagopalpal

FINGERPRINTFINGERPRINT

TOPICS COVEREDTOPICS COVERED Sensors UsedSensors Used RepresentationsRepresentations Matching AlgorithmsMatching Algorithms State of ArtState of Art Research ProblemsResearch Problems


Sensors UsedSensors Used


Basic TypesBasic Types

Optical Sensors Optical Sensors – Oldest and most widely usedOldest and most widely used

Solid State SensorsSolid State Sensors Thermal Based SensorsThermal Based Sensors Pressure Based SensorsPressure Based Sensors

– Recent : rarely usedRecent : rarely used Ultrasonic Based SensorsUltrasonic Based Sensors

– Recent : rarely usedRecent : rarely used


Optical SensorsOptical Sensors

The finger is placed on a coated plateThe finger is placed on a coated plate

Charged Coupled Device (CCD) Charged Coupled Device (CCD) converts the image of the fingerprint converts the image of the fingerprint

It also takes a picture of the dirt, It also takes a picture of the dirt, greases, and contamination found on greases, and contamination found on the fingerthe finger



The process, referred to as The process, referred to as

‘‘Frustrated Total Internal Reflection’Frustrated Total Internal Reflection’



Dirty Fingerprints cannot use system Dirty Fingerprints cannot use system effectivelyeffectively

Latent prints are leftover prints from Latent prints are leftover prints from previous usersprevious users

No ESD issues No ESD issues

Durable to incidental damage Durable to incidental damage


Solid State Capacitance Solid State Capacitance SensorsSensors

The sensor uses solid-state The sensor uses solid-state capacitance sensing to capture unique capacitance sensing to capture unique fingerprint datafingerprint data

Finger as one plateFinger as one plate Surface of sensor as other plateSurface of sensor as other plate Sensor surface - silicon chip containing Sensor surface - silicon chip containing

an array of 90,000 capacitor plates an array of 90,000 capacitor plates with sensing circuitry at 500-dpi pitch with sensing circuitry at 500-dpi pitch



Veridicom – one of the leading playersVeridicom – one of the leading players

Easy Integration into a variety of Easy Integration into a variety of electronics electronics



Very difficult to spoofVery difficult to spoof.. Immune to day-to-day fingerprint Immune to day-to-day fingerprint

variationsvariations Low powerLow power Immune to ambient lightImmune to ambient light High image qualityHigh image quality Scratch resistantScratch resistant


Thermal BasedThermal Based

Infrared to sense the temperature Infrared to sense the temperature differences between the ridges and differences between the ridges and valleys of the finger to create a valleys of the finger to create a fingerprint image fingerprint image

Temperature differential between the Temperature differential between the skin ridges and the air caught in the skin ridges and the air caught in the fingerprint valleys fingerprint valleys

No latent printsNo latent prints Good Quality ImagesGood Quality Images


Thermal BasedThermal Based

Sweeping needs some user skill Sweeping needs some user skill

High power consumption High power consumption to avoid to avoid the possibility of a thermal the possibility of a thermal equilibrium between the sensor and equilibrium between the sensor and the fingerprint surface. the fingerprint surface.

AMTEL – one of the leading playersAMTEL – one of the leading players


Pressure-Based Sensors Pressure-Based Sensors

Principle: Principle: – when a finger is placed over the sensor when a finger is placed over the sensor

area, only the ridges of the Fingerprint area, only the ridges of the Fingerprint come in contact with the sensor piezo come in contact with the sensor piezo arrayarray

pressure sensors generate a 1-bit pressure sensors generate a 1-bit binary image binary image


Pressure Based SensorsPressure Based Sensors

Works well with Dry as well as Wet skinWorks well with Dry as well as Wet skin

Larger Sensing AreaLarger Sensing Area


Ultra Sound Based Sensors Ultra Sound Based Sensors

Use High Frequency Sound WavesUse High Frequency Sound Waves Transmits acoustic waves and Transmits acoustic waves and

measures the distance based on the measures the distance based on the impedance of Finger, Plate and Air impedance of Finger, Plate and Air

Ultrasound can penetrate through Ultrasound can penetrate through many mediums many mediums

Considered perhaps the most accurate Considered perhaps the most accurate of the fingerprint technologies of the fingerprint technologies


Acquisition ProblemsAcquisition Problems

Regular Scratches Regular Scratches Skin Peeling due to weather Skin Peeling due to weather

conditionsconditions Natural Permanent creasesNatural Permanent creases Temporary CreasesTemporary Creases Dirty FingersDirty Fingers Long NailsLong Nails Ethnic TraitEthnic Trait


Feature ExtractionFeature Extraction


Fingerprint FeaturesFingerprint Features

ClassificationClassification Distinguishing CharacteristicsDistinguishing Characteristics


Fingerprint ClassificationFingerprint Classification

On the basis on ridge flow patternsOn the basis on ridge flow patterns Arch, Tented Arch, Whorl and Loop Arch, Tented Arch, Whorl and Loop

(Right/Left)(Right/Left)


Distinguishing FeaturesDistinguishing FeaturesRidge Features and their Ridge Features and their

PositionPosition


MINUTIAEMINUTIAE

Points where ridges terminate, Points where ridges terminate, bifurcate bifurcate

or merge with each other are called or merge with each other are called minutiae points minutiae points

In law enforcement 12 -16 matching In law enforcement 12 -16 matching

minutiae are sufficient to match a minutiae are sufficient to match a

personperson


Image EnhancementImage Enhancement

Noise in fingerprint may be due to dry or Noise in fingerprint may be due to dry or wet skin, dirt, cut or noise of capture wet skin, dirt, cut or noise of capture devicedevice

Enhancement operationsEnhancement operations Adaptive Matched Filter – to enhance Adaptive Matched Filter – to enhance

ridges oriented in the same direction as ridges oriented in the same direction as those in the same localitythose in the same locality

Adaptive Thresholding (binarization)Adaptive Thresholding (binarization)


Minutiae Extraction Minutiae Extraction AlgorithmAlgorithm


Feature ExtractionFeature Extraction

Original Grey level Original Grey level

imageimage

Orientation of the Orientation of the ridges ridges calculated by calculated by

Fourier transformFourier transform


Feature Extraction (Contd)Feature Extraction (Contd)

Segmentation into Segmentation into foreground and foreground and backgroundbackground

Masking out the Masking out the background is done background is done in order to retrieve in order to retrieve the ridges the ridges


Feature extraction (Contd)Feature extraction (Contd) Finally minutiae points Finally minutiae points

are calculated from are calculated from the ridge imagethe ridge image

Endings have 1 Endings have 1 adjacent black pixel adjacent black pixel ( 8 neighborhood )( 8 neighborhood )

Bifurcations have Bifurcations have more than 2 adjacent more than 2 adjacent black pixelsblack pixels

Finally the minutiae Finally the minutiae points are points are superimposed on the superimposed on the original imageoriginal image


Feature extraction (Contd)Feature extraction (Contd)

Minutiae extracted are represented Minutiae extracted are represented by by

- Their (x,y) coordinate- Their (x,y) coordinate

- Orientation (- Orientation (ΘΘ))

- Forming a 3 tuple (x, y, - Forming a 3 tuple (x, y, ΘΘ))

- Also the type of minutiae i.e. Ridge - Also the type of minutiae i.e. Ridge ending, ridge bifurcation could be ending, ridge bifurcation could be stored.stored.


Chain coded Ridge Extraction Chain coded Ridge Extraction MethodMethod

By Dr Venugopal, Zhixin Shi & John SchneiderBy Dr Venugopal, Zhixin Shi & John Schneider


Chain coded Ridge Extraction Chain coded Ridge Extraction MethodMethod

By Dr Venugopal, Zhixin Shi & John SchneiderBy Dr Venugopal, Zhixin Shi & John Schneider

PPinin – vector leading to candidate point P from several – vector leading to candidate point P from several previous neighboring contour pointsprevious neighboring contour points

Similarly PSimilarly Poutout

Calculate S(PCalculate S(Pin in , P, Poutout) < x1y2 – x2y1) < x1y2 – x2y1 S(PS(Pin in , P, Poutout) > 0 Left Turn and S(P) > 0 Left Turn and S(Pin in , P, Poutout) < 0 Right Turn ) < 0 Right Turn ThresholdThreshold


Tessellated approachTessellated approach Equal sized non-overlapping windows overEqual sized non-overlapping windows over the image and normalizing pixel intensitiesthe image and normalizing pixel intensities within the window to constant mean and variance.within the window to constant mean and variance. Windows of size 30*30Windows of size 30*30 Bank of 8 Gabbor filters is applied to each Bank of 8 Gabbor filters is applied to each

windowwindow Absolute average deviation of intensity in each Absolute average deviation of intensity in each

filtered cell is treated as a feature valuefiltered cell is treated as a feature value Thus 8 Feature values for each cell Thus 8 Feature values for each cell Feature values from all cells concatenated inorder Feature values from all cells concatenated inorder

to form feature vector of the image.to form feature vector of the image. For a 300 * 300 image – 648d feature vector.For a 300 * 300 image – 648d feature vector.


Matching AlgorithmsMatching Algorithms Fingerprints represented by Minutiae Fingerprints represented by Minutiae pointspoints Simplest Method: “Simplest Method: “Point Pattern Point Pattern MatchingMatching”” Requirement:Requirement:

– Correspondence between Template and Correspondence between Template and InputInput– No DeformationsNo Deformations– Every Minutiae LocalizedEvery Minutiae Localized

Not RealisticNot Realistic


Matching AlgorithmsMatching Algorithms

Requirement of the Matching Model:Requirement of the Matching Model:– Different LocationsDifferent Locations– Different OrientationsDifferent Orientations– Different PressureDifferent Pressure– Spurious MinutiaeSpurious Minutiae– Missing Genuine MinutiaeMissing Genuine Minutiae– Linear / Non-linear perturbation of pair Linear / Non-linear perturbation of pair

of minutiaeof minutiae


Matching AlgorithmsMatching Algorithms

Different ApproachesDifferent Approaches

– Image BasedImage Based– Graph BasedGraph Based– Ridge BasedRidge Based– Minutiae BasedMinutiae Based


Point Based MatchingPoint Based Matching

#1 . Relaxation Method:#1 . Relaxation Method:– Iteratively adjust confidence levelIteratively adjust confidence level– Inherently slow due to Iterative propertyInherently slow due to Iterative property

#2. Hough Transform Method#2. Hough Transform Method– Detecting Peaks in Transformation Detecting Peaks in Transformation

parameter Spaceparameter Space– If only a few minutiae points, difficult to If only a few minutiae points, difficult to

accumulate enough evidence for a accumulate enough evidence for a matchmatch


Point Pattern MatchingPoint Pattern Matching#3. Energy Minimization Approach#3. Energy Minimization Approach

– Correspondence between pair of points by Correspondence between pair of points by using an energy functionusing an energy function

– Energy function based on initial set of Energy function based on initial set of possible correspondencespossible correspondences

– Very Slow Very Slow unsuitable for real-time unsuitable for real-time applns.applns.

#4. Tree-pruning Approach#4. Tree-pruning Approach– Search over a tree of possible matchesSearch over a tree of possible matches– Strict requirements: equal number of pointsStrict requirements: equal number of points– Impractical requirements Impractical requirements


Point Pattern MatchingPoint Pattern Matching

Alignment BasedAlignment Based

– Alignment StageAlignment Stage Transformations determined for alignmentTransformations determined for alignment

– Matching StageMatching Stage Elastic String Matching AlgorithmElastic String Matching Algorithm


Alignment Based MatchingAlignment Based Matching

ALIGNINGALIGNING– Corresponding point pairsCorresponding point pairs

– Exhaustive testExhaustive test Large Number of testsLarge Number of tests Impractical though FeasibleImpractical though Feasible

– Aligning Minutiae by aligning RidgesAligning Minutiae by aligning Ridges


Ridge AlignmentRidge Alignment


Post Alignment MatchingPost Alignment Matching

Counting the number of overlapping Counting the number of overlapping points – if exact overlappoints – if exact overlap

Elastic Algorithm – tolerating Elastic Algorithm – tolerating deformationdeformation– Bounding BoxBounding Box– Minutiae Points as StringsMinutiae Points as Strings– Dynamic Programming approach for String Dynamic Programming approach for String

Matching ( edit distances )Matching ( edit distances )– Distance measure Distance measure penalty for a mismatch penalty for a mismatch– Adaptive Bounding BoxAdaptive Bounding Box


Ridge Based MatchingRidge Based Matching

Correlation Based Correlation Based compare the compare the global patterns: Ridge and Furrowsglobal patterns: Ridge and Furrows

Don’t perform very well due to noisy Don’t perform very well due to noisy ImagesImages

Invariant Representation neededInvariant Representation needed– Strength of Ridges at various orientationsStrength of Ridges at various orientations– 2D Gabor wavelets2D Gabor wavelets



Parameters:f -> Frequency Ridge FrequencySx, Sy -> Standard DeviationsTheta -> Orientation



Each of 8 Gabor Filters appliedEach of 8 Gabor Filters applied

Standard Deviation Map for each of 8 Standard Deviation Map for each of 8 ImagesImages

For Alignment,For Alignment,– Weighted CorrelationWeighted Correlation

Euclidean Distance measureEuclidean Distance measure


Graph Based MatchingGraph Based Matching

Clustering Techniques usedClustering Techniques used Homogeneous RegionsHomogeneous Regions

– Regions with similar DirectionRegions with similar Direction Using these regions, develop Using these regions, develop

‘Relational Graphs’‘Relational Graphs’ invariant with respect to translation invariant with respect to translation

and rotationand rotation Tolerates Partial MatchesTolerates Partial Matches


Multilevel MatchingMultilevel Matching

Text BasedText Based– Textual Fields:Textual Fields:

age range / color of hair and eyeage range / color of hair and eye

Class BasedClass Based– 5 classes of Fingerprints5 classes of Fingerprints

Ridge – Density BasedRidge – Density Based– Count of the ridgesCount of the ridges

Elastic MatchingElastic Matching


Performance EvaluationPerformance Evaluation

FVC 2004 Fingerprint Verification FVC 2004 Fingerprint Verification CompetitionCompetition

4 databases – 2 optical, 1 thermal 4 databases – 2 optical, 1 thermal sweeping sensor and 1 syntheticsweeping sensor and 1 synthetic

REJ, FMR, FNMR, ROC, REJ, FMR, FNMR, ROC, Genuine/Imposter distributionGenuine/Imposter distribution

Enrollment time, Matching time, Enrollment time, Matching time, average and maximum template average and maximum template size, memory allocatedsize, memory allocated


Best AlgorithmBest Algorithm

Winner of FVC2002 – Bioscrypt Inc.Winner of FVC2002 – Bioscrypt Inc. Ridge patterns not ridge endingsRidge patterns not ridge endings Pattern based templates not minutiae Pattern based templates not minutiae

basedbased correlation of ridge patternscorrelation of ridge patterns Heavy weights to areas where images Heavy weights to areas where images

are clear and highly complexare clear and highly complex Incompatible with minutiae based Incompatible with minutiae based

systemssystems


Pressure based SystemsPressure based Systems

Pressure sensitive Pressure sensitive Wet or dry fingersWet or dry fingers Captures print of the finger not just image Captures print of the finger not just image

of the printof the print By Elform OEM Inc.By Elform OEM Inc.


Ultrasonic Fingerprint Ultrasonic Fingerprint TechnologyTechnology

Sound waves reflecting off ridges and Sound waves reflecting off ridges and valleys on the fingervalleys on the finger

Oblivious to dirt, grease, ink, Oblivious to dirt, grease, ink, moisture, grime, or other substances moisture, grime, or other substances routinely found on fingers which routinely found on fingers which cause the most false readingscause the most false readings

Fingerprints of childrenFingerprints of children


Ultrasonic Fingerprint Ultrasonic Fingerprint TechnologyTechnology


Fingerprinting ChildrenFingerprinting Children


Research ProblemsResearch Problems


Research Problems (1)Research Problems (1)

#1 Acquisition Problems:#1 Acquisition Problems:

– Image acquisition susceptible to noiseImage acquisition susceptible to noise

– SOLUTION:SOLUTION:

Sensors capable of capturing Fingerprint Sensors capable of capturing Fingerprint Image invariant of noiseImage invariant of noise


#2 Enhancement Problems:#2 Enhancement Problems:

– The Gray Scale Image obtained has to The Gray Scale Image obtained has to be enhanced for further processingbe enhanced for further processing

– SOLUTION:SOLUTION: Better Binarization AlgorithmsBetter Binarization Algorithms More Effective Representation Schemes of More Effective Representation Schemes of

FingerPrint ImagesFingerPrint Images


#3 Features to be Extracted#3 Features to be Extracted– Deciding the exact features for Deciding the exact features for

matchingmatching – Only Global or Local or bothOnly Global or Local or both

– SOLUTION:SOLUTION: A comparative study of each Feature A comparative study of each Feature

combinations for determining Individualitycombinations for determining Individuality


#4 Feature Extraction#4 Feature Extraction

– The feature Extraction Algorithm should The feature Extraction Algorithm should be robust to noisebe robust to noise

– Should detect false featuresShould detect false features– Should capture Maximum possible Should capture Maximum possible

featuresfeatures


#5 Partial Matches#5 Partial Matches

– Only a few Feature Points capturedOnly a few Feature Points captured

– SOLUTIONSOLUTION Matching Algorithm Based upon trying to Matching Algorithm Based upon trying to

Match using a subset of actual Feature Match using a subset of actual Feature pointspoints


Fingerprint ClassificationFingerprint Classification

To search large databases efficientlyTo search large databases efficiently exclusive classificationexclusive classification 90% in three classes90% in three classes Continuous ClassificationContinuous Classification Fingerprints not classified into non Fingerprints not classified into non

overlapping classesoverlapping classes Instead as a numerical vector (by K-Instead as a numerical vector (by K-

L Transform)L Transform)


E- Commerce applicationsE- Commerce applications Fingerprint generationFingerprint generation multimodal biometrics (e.g., multimodal biometrics (e.g.,

combination of fingerprints and combination of fingerprints and faces), faces),

combination of multiple matcherscombination of multiple matchers digital watermarking of fingerprints digital watermarking of fingerprints

by amit mhatre and roshith rajagopal fingerprint topics covered sensors used sensors used...

Documents

roshith rajagopal sensors

roshith rajagopal slide

amit mhatre

finger slide

plate finger

pitch slide

fingerprint image infrared

unique fingerprint data