Fingerprint Matching Chapter 4, sections 4.4-4.8 Handbook of fingerprint recognition

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Fingerprint MatchingChapter 4, sections
4.4-4.8Handbook of fingerprint
recognitionFilterbank-Based Fingerprint
MatchingJain A.K. Prabhakar S., Jonh L. and
Pankanti S., IEEE Trans. On Image Processing,
vol. 9, No. 5, 2005.

• Alireza Tavakkoli

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Outline

• Fingerprint Matching
• Global vs Local Minutiae Matching.
• Dealing with Distortion.
• Ridge Feature-based Matching Techniques.
• Comparing the Performance.

• Filterbank-based Matching
• Motivation
• Filter-based feature extraction
• Reference point location.
• Filtering
• Feature vectors
• Matching
• Experimental results

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Global vs. Local Minutiae Matching

• Trade offs
• Simplicity, low cost, high distortion tolerance.
• High distinctiveness.
• Hrechak and McHugh (1990)
• Eight dimensional feature vector
• Minutiae dots, ridge endings, ridge
  bifurcations, islands, spurs, crossovers, bridges
  and short ridges.
• Invariant to fingerprint alignments.
• Practical applicability!!!!!!

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Global vs. Local Minutiae Matching

• Chen and Kuo (1991), Wahab, Chin and Tan (1998)
• Enriched local structures proposed by Harchak and
  McHugh in 1990.
• Distance
• The ridge count
• Relative orientation of each surrounding minutiae
  with the central one.
• Angle between orientation of the line connecting
  each minutiae to central one and its orientation.
• Comparing local structures by correlation or
  tree-matching.
• Fan, Liu and Wang(2000) (Geometric Clustering)
• Each cluster ? rectangular bonding box.
• Using a fuzzy bipartite weighted graph matching.
• Willis and Myers (2001)
• Minutiae counting in a dart board pattern of
  wedges and ridges.
• Partially invariant to rotation and translation.

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Global vs. Local Minutiae Matching

• Jiang and Yau, Ratha et. al. (2000) (Using
  both methods advantages)
• Fast local matching for recovering alignments.
• Consolidation stage.
• Jiang
  Ratha

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Variants of the 2 Stage Algorithm

• Zhang and Wang (2002)
• Using Core points
• speed up the initial local structure matching.

• Lee, Choi and Kim (2002)
• Using more minutiae pairs
• Guide the consolidation step.
• Robustness
• Normalization.

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More Local Minutiae Matching Methods

• Maio and Maltoni (1995) and Kovac-Vajna (2000)
• Enhancement and accurate minutiae extraction only
  on template.
• Extraction of minutiae template T.
• Locally checking correspondence in verification
  stage.
• Maio
• Gray level minutiae extraction.
• Locally tracking the ridges in verification for
  finding correspondence.

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Kovac Algorithm

• Kovac
• 16x16 neighborhood of minutiae in T correlated by
  I ? list of candidate positions.
• Triangular matching
• Start with 2 minutiae in T and candidate
  positions in I.
• Expanding the list by adding a pair of minutiae
  and candidate.
• Consolidation
• Checking the correspondence of gray scale
  profiles between every pair.
• 1) Ridge count.
• 2) Dynamic time warping (Handle small
  perturbations).

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Dealing with Distortion

• One of the most critical intra-class variability.
  (NIST 24)
• Mechanical force sensor ? less distortion
• Automatic detection of distortion from videos.
• Distortion-tolerant matchers
• Both of the above solutions are difficult to
  implement in commercial sensing systems.

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How to deal with distortion?

• Relaxing spatial relationships between minutiae
• Global matching techniques
• Tolerance boxes (spheres)
• High distortion ? larger Boxes ? high false match
• Polar coordinate boxes (Jain (97) and Luo
  (2000))
• Edit distance for matching pre-aligned minutiae.
• Size of boxes increase by the distance from
  center.
• Kovac method
• Triangular matching can tolerate large global
  distortions.
• Adding this small differences may be large!!!!
• Non of the above explicitly address the problem!

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Dealing with Distortion

• Almansa and Cohen (2000)
• A 2D warping algorithm (mapping FP patterns)
• Controlling warping by minimizing and energy
  function.
• Two minutiae spatially coincide.
• Penalty term ? increasing by the irregularity of
  the warping.
• Two step iterative algorithm to minimize energy.
• Problem with convergence!!!

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Dealing with Distortion

• Bazen and Gerez (2002)
• Smoothed mapping between template and input
  minutiae.
• Algorithm
• Initially computing minutiae through a local
  approach and consolidation step.
• Reduction of the size of tolerance box
• Use of a thin spline model to deal with
  non-linear distortion.
• Locally moving minutiae in input image to best
  fit the template minutiae, iteratively.
  (According to the model smoothness constrains)
• Significant improvements achieved.

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Normalization to canonical form Senior and Bolle
(2001)
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Normalization Techniques

• Lee Chi and Kim (2002)
• Normalization during the matching stage
• Normalization according to local ridge frequency.
• Distortion ? increase in distance between
  minutiae ? local ridge frequency decreases ?
  Normalization can compensate for that.
• Problem
• Far apart ridges ? normalization may have higher
  distortion errors than the distortion itself.

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Modeling Skin Distortion Maio
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Distortion Recovery
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Ridge Feature-based Matching Techniques

• Why?
• Difficulty in reliable minutiae extraction from
  poor quality images.
• Time consuming.
• Use of additional features increases the accuracy
  and robustness.
• Alternative features
• Size and silhouette. (unstable)
• Singularities. (unstable)
• Spatial relationship. (tree grammars,
  incremental graph matching)
• Shape features. (1D signature from 2D,
  used with minutiae-based)
• Global/local texture. (Texture properties? from
  ridge lines)
• Sweat pores. (Very discriminative but
  expensive)
• Fractal features.

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Fingerprint Texture Analysis

• Analyzing texture in furrier domain (Coetzee and
  Botha (93) and Willis and Myers (2001))
• Spatial fingerprint texture? Almost constant in
  frequency domain.
• Small deviations from the dominant frequency ?
  minutiae!!
• Wedge-ring detector.
• Accumulating the harmonic of individual regions.
• Global texture analysis ? all regions into one
  measurement ? Loss of spatial information.
• Filterbank-based Analysis of Fingerprint (Jain
  (2000))
• Topic of next talk (!).

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Comparing Performance

• Various fingerprint matching techniques.
• Which one is the best algorithm?
• Performance involves a Trade off among different
  measures.
• Performance relates to difficulty of the
  benchmark ? lack of a global one.
• Before FVC? NIST Databases ? not good for
  live-scan.
• NIST 4, 10, 14 Rolled inked impressions.
• NIST 24 Videos.
• NIST 27 Latent fingerprints.
• FVC2000/02 (can be found on the DVD of the
  book)

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Typical Mistakes

• Using the same datasets for trainig, validation
  and testing.
• Computing performance on very small dataset.
• Cleaning the dataset by removing rejected or
  misclassified samples.
• Claiming better classification while using
  different datasets.
• Hiding the weak points of an algorithm/
  Documenting its failures.

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Second Talk

• Filterbank-Based Fingerprint Matching
• Jain A.K. Prabhakar S., Jonh L. and Pankanti S.
  IEEE Trans. On Image Processing, vol. 9, No. 5,
  2005.

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Outline

• Motivation
• Filter-based feature extraction
• Reference point location.
• Filtering
• Feature vectors
• Matching
• Experimental results

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Introduction

• Extraction and explicit detection of complete
  ridge structures!???
• Use of components of rich discriminatory
  information.
• Local ridge structures.
• Matching fingerprints with different number of
  registered minutiae.

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Overview

• Single reference point
• Assuming the vertical alignment.
• Rotation invariance can be achieved by a cyclic
  rotation of the extracted feature values.
• Tessellation of region of interest around
  reference point.
• Filtering the region of interest in 8 direction
  using Gabor filter-banks.
• Computation of the Average Absolute Deviation
  (AAD) of gray values in each sector.
• Generation of the Finger Code.

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Overview
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Reference Point Location

• Using conspicuous landmarks to locate reference
  point.
• Point of maximum curvature of concave ridges.

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Reference Point Location (Contd.)

• Multiple resolution analysis of orientation map
• Handling noise in poor quality images
• Using large neighborhoods.
• Accurate localization
• Sensitive to local variations.
• Estimation of Orientation Field.

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Least Square Orientation Estimation

• Divide Image into wxw blocks.
• Compute gradient at each pixel.
• Estimate the local orientation at center of each
  block.

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Reference Point Location Algorithm

• Estimate the orientation field described above.
• Smooth the orientation field in a local
  neighborhood
• Use a continuous vector field.
• Compute the sine component of the smoothed
  orientation field, (E)
• Initialize a label image, (A).

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Reference Point Location Algorithm

• For each pixel in the E, integrate the values of
  region RI and RII and compute
• Find maximum of A and assign its coordinate to
  core.
• Perform algorithm for a fixed number of times
  with less window sizes.

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Localization of Core Point
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Tessellation of Region of Interrest
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Filtering

• Gabor filters
• Remove noise.
• Preserve true ridge and valley structures
• Provide directional information.
• Minutiae
• Anomaly in local parallel ridges.

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Filtering Stages

• Normalization
• Even Symmetric Gabor Filter
• Mask 32x32.
• Ferq. 1/k
• Angels

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Filtering Results
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Feature Vector

• Average Absolute Deviation

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How Discriminatory?
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Matching

• Euclidian distance.
• Translation Invariance
• Reference Point
• Rotation Invariance
• Approximated by cyclic rotation of Finger Codes.
• Generating 11.25 degree rotated image in
  registration stage.

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Experiments

• Database 1 (MSU-DBI)
• 167 subjects.
• Digital Biometrics optical sensor.
• Image size 508x480
• 35 women.
• 46.5 under 25.
• 50.51 between 25 and 50.
• 2.5 older than 50.
• Two impressions taken from four finger.
• A second round of collection after 6 weeks.
• Total database size 2672 images.
• Live feedback at collection time ? well centered
  images.
• Distortion in data collected after 6 weeks ?
  Challenging.

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Experiments

• Database 2 (NIST 9 Vol. 1 CD 1)
• 1800 images.
• 900 different fingers.
• 832x768

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Experiments

• MSU-DBI
• Rejected 100(4)
• Why?
• Ref point at corner.
• Poor Quality. (dryness)

• NIST 9
• Rejected 100 (5.6)
• Why?
• The same reasons.

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Genuine and Imposter Probabilities
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Experiments
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ROC curve (MSU-DBI)
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ROC Curve (NIST9)
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Observations

• Most of false accepts are among the same type.
• Good for indexing.
• Captures the discriminatory information.
• Good for combining with minutiae.
• Combination by Neyman-Pearson Rule.

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Neyman-Pearson Rule
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• Questions?