1 Fingerprint Analysis and Representation Handbook of Fingerprint Recognition Chapter III Sections 1-6 Presentation by: Tamer Uz Adaptive Flow Orientation

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Fingerprint Analysis and Fingerprint Analysis and RepresentationRepresentation

Handbook of Fingerprint RecognitionHandbook of Fingerprint RecognitionChapter III Sections 1-6Chapter III Sections 1-6

Presentation by: Tamer UzPresentation by: Tamer Uz

Adaptive Flow Orientation based Adaptive Flow Orientation based Feature Extraction in Fingerprint Feature Extraction in Fingerprint

ImagesImagesN.K. Ratha, S. Chen, A.K. Jain, Pattern N.K. Ratha, S. Chen, A.K. Jain, Pattern

Recognition, vol. 28, no. 11, pp. 1657-1672, Recognition, vol. 28, no. 11, pp. 1657-1672, 1995. 1995.

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Fingerprint Analysis and Fingerprint Analysis and RepresentationRepresentation

Handbook of Fingerprint RecognitionHandbook of Fingerprint Recognition

Chapter III Sections 1-6Chapter III Sections 1-6

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OutlineOutline

IntroductionIntroduction Estimation of Local OrientationEstimation of Local Orientation Estimation of Local Ridge FrequencyEstimation of Local Ridge Frequency SegmentationSegmentation Singularity and Core DetectionSingularity and Core Detection

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IntroductionIntroduction

FingerprintFingerprintInterleaved Interleaved ridges and ridges and valleysvalleys

Ridge width: Ridge width: 100100μμm-300 m-300 μμmm

Ridge-valley Ridge-valley cycle: 500 cycle: 500 μμmm

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A Global LookA Global LookSingularities:Singularities: In the global level the fingerprint pattern In the global level the fingerprint pattern

shows some distinct shapesshows some distinct shapes• Loop ( )Loop ( )• Delta (Delta (ΔΔ))• Whorl (O)…Two facing loopWhorl (O)…Two facing loop

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A Global LookA Global Look

Core:Core: A reference point for the alignment.A reference point for the alignment.The northmost loop type singularity. The northmost loop type singularity.

According to Henry(1900), it is the According to Henry(1900), it is the northmost point of the innermost northmost point of the innermost ridgeline. ridgeline.

Not all fingerprints have a core (Arch type Not all fingerprints have a core (Arch type fingerprints)fingerprints)

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A Global LookA Global LookSingular regions are commonly used for fingerprint classification:Singular regions are commonly used for fingerprint classification:

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Local LookLocal LookMinutia: Small details. Discontinuties in the ridges. (Sir Francis Minutia: Small details. Discontinuties in the ridges. (Sir Francis

Galton) Galton)

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Local LookLocal Look

Ridge ending / ridge bifurcation dualityRidge ending / ridge bifurcation duality

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Local LookLocal LookSweat PoresSweat Pores

• High resolution images (1000 dpi)High resolution images (1000 dpi)• Size 60-250 Size 60-250 μμmm• Highly distinctiveHighly distinctive• Not practical (High resolution, good quality images) Not practical (High resolution, good quality images)

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Estimation of Local Ridge Estimation of Local Ridge OrientationOrientation

Quantized mapQuantized map Average orientation around indices i,jAverage orientation around indices i,j Unoriented directionsUnoriented directions Weighted (rWeighted (rijij))

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Simple ApproachSimple Approach• Gradient with Sobel Gradient with Sobel

or Prewitt operatorsor Prewitt operators

• ΘΘijij is orthogonal to is orthogonal to the direction of the the direction of the gradientgradient

Drawbacks:Drawbacks:• Non-linear and discontinuous around 90Non-linear and discontinuous around 90• A single estimate is sensitive to noiseA single estimate is sensitive to noise• Circularity of angles: Averaging is not possibleCircularity of angles: Averaging is not possible• Averaging is not well defined.Averaging is not well defined.

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Averaging Gradient EstimatesAveraging Gradient Estimates

(Kass, Witkin 1987)(Kass, Witkin 1987)

ddijij = [r = [rijij.cos2.cos2θθijij, r, rijijsin2 sin2 θθijij]]

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Reliability (rReliability (rijij))

calculated according to variance or least sq. residuecalculated according to variance or least sq. residue Like detecting outliers and assigning low weights to them.Like detecting outliers and assigning low weights to them.

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Effect of averagingEffect of averaging

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Estimation of Local Ridge Estimation of Local Ridge FrequencyFrequency

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Simple AlgorithmSimple Algorithm

1)1) 32x16 oriented window centered at [x32x16 oriented window centered at [xii, y, yii] ]

2)2) The x-signature of the grey levels is obtainedThe x-signature of the grey levels is obtained

3)3) ffijij is the inverse of the average distance is the inverse of the average distance

To handle noise interpolation and/or low pass To handle noise interpolation and/or low pass filtering is applied. filtering is applied.

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Other AlgorithmsOther Algorithms• Mix-spectrum technique (Jiang, 2000)Mix-spectrum technique (Jiang, 2000)

Energy of 2Energy of 2ndnd and 3 and 3rdrd harmonics in the spectrum harmonics in the spectrum (Fourier) domain is imposed on the fundamental (Fourier) domain is imposed on the fundamental frequency. frequency.

• Variation function technique (Maio Maltoni Variation function technique (Maio Maltoni 1998a)1998a)

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Example on Variation Function Tech.Example on Variation Function Tech.

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SegmentationSegmentation Segmentation MethodsSegmentation Methods

• Orientation histogram in neighborhood. Orientation histogram in neighborhood.

• Variance orthogonal to the ridge direction Variance orthogonal to the ridge direction

• Average magnitude of gradient in blocksAverage magnitude of gradient in blocks

• Threholding the variance of Gabor Filter (Band-pass) Threholding the variance of Gabor Filter (Band-pass) responces.responces.

• Classifying pixels as forground or background using Classifying pixels as forground or background using gradient coherence, intensity mean and intensity gradient coherence, intensity mean and intensity vaience as featuresvaience as features

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SegmentationSegmentation

Example Example SegmentationSegmentation

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Singularity and Core DetectionSingularity and Core Detection

Singularity Detection MethodsSingularity Detection Methods

• Poincare methodPoincare method

• Methods based on local characteristics Methods based on local characteristics of the orientation imageof the orientation image

• Partitioning based methodsPartitioning based methods

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Poincare MethodPoincare Method

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Singularity and Core DetectionSingularity and Core Detection Poincare MethodPoincare Method

If we know the type of the fingerprint beforehand, If we know the type of the fingerprint beforehand, false singularities can be eliminated by iteratively false singularities can be eliminated by iteratively smoothing the image with the help of the smoothing the image with the help of the following observation:following observation:

• Arch fingerprints do not contain singularitiesArch fingerprints do not contain singularities

• Left loop, right loop and tented arch fingerprints contain Left loop, right loop and tented arch fingerprints contain one loop and one deltaone loop and one delta

• Whorl fingerprints contain two loops and two deltasWhorl fingerprints contain two loops and two deltas

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Methods based on local featuresMethods based on local features• Orientation histograms at local levelOrientation histograms at local level• IrregularityIrregularity

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Partitioning based methodsPartitioning based methods

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Core Detection:Core Detection:

CoreCore: North most loop type singularity: North most loop type singularity

• It is generally used for fingerprint registrationIt is generally used for fingerprint registration

• It needs to be found for the arches from It needs to be found for the arches from scratchscratch

• Has to be validated for the othersHas to be validated for the others

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Core DetectionCore Detection

Popular Algorithm (Wegstein 1982):Popular Algorithm (Wegstein 1982):• Orientation image is searched row by rowOrientation image is searched row by row• The sextet best fits a certain criteria is found The sextet best fits a certain criteria is found

and the core is interpolatedand the core is interpolated• AccurateAccurate• Complicated and heuristicComplicated and heuristic

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Core DetectionCore Detection

Other idea:Other idea:• Voting based line intersectionVoting based line intersection

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Adaptive Flow Orientation Adaptive Flow Orientation based Feature Extraction in based Feature Extraction in

Fingerprint ImagesFingerprint Images

N.K. Ratha, S. Chen, A.K. Jain, N.K. Ratha, S. Chen, A.K. Jain, Pattern Recognition, vol. 28, Pattern Recognition, vol. 28, no. 11, pp. 1657-1672, 1995. no. 11, pp. 1657-1672, 1995.

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OutlineOutline

IntroductionIntroduction Related WorkRelated Work Proposed AlgorithmProposed Algorithm Experimental ResultsExperimental Results ConclusionConclusion

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This paper proposes a feature extraction This paper proposes a feature extraction method from fingerprint images.method from fingerprint images.

Extracted features are minutiae (x,y,Extracted features are minutiae (x,y,ΘΘ))

Method: Extracting orientation field Method: Extracting orientation field followed by segmentation and analysis of followed by segmentation and analysis of the ridgesthe ridges

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General Stages of the Feature General Stages of the Feature Extraction ProcessExtraction Process• PreprocessingPreprocessing• Direction ComputationDirection Computation• BinarizationBinarization• ThinningThinning• PostprocessingPostprocessing

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Related WorkRelated Work

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Proposed AlgorithmProposed Algorithm

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1)Preprocessing and Segmentation1)Preprocessing and Segmentation

Goal: To obtain binary segmented ridge images.Goal: To obtain binary segmented ridge images.

Steps:Steps:• Computation of orientation fieldComputation of orientation field• Foreground/background separationForeground/background separation• Ridge segmentationRidge segmentation• Directional smoothing of the ridgesDirectional smoothing of the ridges

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Proposed AlgorithmProposed Algorithm1.1 Computation of the Orientation Field1.1 Computation of the Orientation Field

An orientation is calculated for each 16x16 blockAn orientation is calculated for each 16x16 block

Steps:Steps:• Compute the gradient of the smoothed block. GCompute the gradient of the smoothed block. Gxx(i,j) and G(i,j) and Gyy(i,j) using 3x3 (i,j) using 3x3

Sobel MasksSobel Masks

• Obtain the dominant direction in the block using the following equation:Obtain the dominant direction in the block using the following equation:

• Quantize the angles into 16 directions.Quantize the angles into 16 directions.

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1.1 Computation of the Orientation Field1.1 Computation of the Orientation Field

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1.2 Foreground/Background Segmentation1.2 Foreground/Background Segmentation

Variance of grey levels in the direction orthogonal to the Variance of grey levels in the direction orthogonal to the orientation field in each block is calculated. orientation field in each block is calculated.

Assumption: fingerprint area will exhibit high variance, where as Assumption: fingerprint area will exhibit high variance, where as the background and noisy regions will exhibit low variance.the background and noisy regions will exhibit low variance.

Variance can also be used as the quality parameter of the Variance can also be used as the quality parameter of the regions. regions.

High variance (high contrast): good qualityHigh variance (high contrast): good quality

Low variance (low contrast): poor qualityLow variance (low contrast): poor quality

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1.2 Foreground/Background Segmentation1.2 Foreground/Background Segmentation

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1.3 Ridge Segmentation1.3 Ridge Segmentation

• Orientation field is used in each (16x16) windowOrientation field is used in each (16x16) window

• Waveform is traces in the direction orthogonal to the Waveform is traces in the direction orthogonal to the orientationorientation

• Peak and the 2 neighbouring pixels are retainedPeak and the 2 neighbouring pixels are retained

• The retained pixels are assigned with the 1 and the rest The retained pixels are assigned with the 1 and the rest are assigned with 0.are assigned with 0.

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1.4 Directional Smoothing1.4 Directional Smoothing

A 3x7 mask (containing all 1s) is placed along the A 3x7 mask (containing all 1s) is placed along the orientationorientation

The mask enables to count the number of “1”s in The mask enables to count the number of “1”s in the mask area.the mask area.

If the 1s are more than 25 percent of the mask If the 1s are more than 25 percent of the mask area than the ridge point is retained. area than the ridge point is retained.

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2) Minutiae Extraction2) Minutiae Extraction

We are a few steps away We are a few steps away from extracting the from extracting the minutiae.minutiae.

• First ridge map is skeletonized.First ridge map is skeletonized.

• Ridge boundary aberrations resultRidge boundary aberrations resultIn hairy growths.

• It is smoothed by using morphological binary “open” operator

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Morphological binary “open” operatorMorphological binary “open” operator

http://documents.wolfram.com/applications/digitalimage/UsersGuide/Morphology/ImageProcessing6.3.html

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3) Post Processing3) Post Processing• Ridge breaks (insufficient Ridge breaks (insufficient

ink or moist)ink or moist)

• Ridge cross-connections Ridge cross-connections (over-ink, over-moist)(over-ink, over-moist)

• BoundariesBoundaries

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Experimental ResultsExperimental Results Summary of the proceduresSummary of the procedures

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Experimental ResultsExperimental Results Summary of the proceduresSummary of the procedures

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Experimental ResultsExperimental Results

Performance EvaluationPerformance Evaluation• Detected minutiae is compared with the ground Detected minutiae is compared with the ground

truth (extracted by human experts)truth (extracted by human experts)

L: Number of 16x16 windows in the input imagePi: Number of minutiae paired in the ith windowQi: Quality factor of the ith window (good=4, medium=2, poor=1)Di: Number of deleted minutiae in the ith windowIi: Number of inserted minutiae in the ith windowMi: Number of ground truth minutiae in the ith window

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Performance EvaluationPerformance Evaluation• Base Line DistributionBase Line Distribution

Generate same number of random minutiae Generate same number of random minutiae in the foreground of (512x512) image in the foreground of (512x512) image

Calculate the GI.Calculate the GI.

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Performance EvaluationPerformance Evaluation

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ConclusionConclusion Robust feature extraction based on ridge flow Robust feature extraction based on ridge flow

orientationsorientations

Novel segmentation methodNovel segmentation method

An adaptive enhancement of the thinned imageAn adaptive enhancement of the thinned image

Quantitative performance evaluationQuantitative performance evaluation

The execution time must be substantially reducedThe execution time must be substantially reduced

Documents

1 Fingerprint Analysis and Representation Handbook of Fingerprint Recognition Chapter III Sections 1-6 Presentation by: Tamer Uz Adaptive Flow Orientation