Fingerprint Matching
Chapter 4, sections 4.4-4.8
Handbook of fingerprint recognition
&
Filterbank-Based Fingerprint
Matching
Jain A.K. Prabhakar S., Jonh L. and Pankanti S., “IEEE Trans. On Image
Processing”, vol. 9, No. 5, 2005.
Alireza Tavakkoli
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
2
Global vs. Local
Minutiae Matching
• Trade offs:
– Simplicity, low cost, high distortion tolerance.
– High distinctiveness.
• [Hrechak and McHugh (1990)]:
– Eight dimensional feature vector:
vi1 , vi 2 ,, vi8 
vi bifurcations,
– Minutiae: dots, ridge endings, ridge
islands, spurs, crossovers, bridges and short ridges.
– Invariant to fingerprint alignments.
– Practical applicability!!!!!!
3
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.
4
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
Vi  m j | sd mi , m j   d max 
Si  Vi , Ei   
 Ei  eij | i, j, d mi , m j , rcmi , m j , ij 
5
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.
6
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.
7
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).
8
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.
9
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!
10
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!!!
11
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.
12
Normalization to canonical form
Senior and Bolle (2001)
13
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.
14
Modeling Skin Distortion [Maio]
15
Distortion Recovery
16
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.
Singularities.
Spatial relationship.
Shape features.
Global/local texture.
Sweat pores.
Fractal features.
(unstable)
(unstable)
(tree grammars, incremental graph matching)
(1D signature from 2D, used with minutiae-based)
(Texture properties from ridge lines)
(Very discriminative but expensive)
17
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 (!).
18
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)
19
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.
20
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.
21
Outline
• Motivation
• Filter-based feature extraction:
– Reference point location.
– Filtering
– Feature vectors
• Matching
• Experimental results
22
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.
23
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”.
24
Overview
25
Reference Point Location
• Using conspicuous landmarks to locate
reference point.
– Point of maximum curvature of concave ridges.
26
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.
27
Least Square Orientation
Estimation
• Divide Image into wxw blocks.
• Compute gradient at each pixel.
• Estimate the local orientation at center of each
block.
28
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).
29
Reference Point Location Algorithm
• For each pixel in the E, integrate the values of
region RI and RII and compute:
Ai, j    E i, j    E i, j 
RI
RII
• Find maximum of A and assign its coordinate to
core.
• Perform algorithm for a fixed number of times
with less window sizes.
30
Localization of Core Point
31
Tessellation of Region of Interrest
32
Filtering
• Gabor filters:
– Remove noise.
– Preserve true ridge and valley structures
– Provide directional information.
• Minutiae:
– Anomaly in local parallel ridges.
33
Filtering Stages
• Normalization:
• Even Symmetric
Gabor Filter:
– Mask 32x32.
– Ferq. = 1/k
– Angels:   0 ,22.5 ,45 ,,157.5
34
Filtering Results
35
Feature Vector
• Average Absolute Deviation:
1
Vi 
ni

  Fi x, y   Fi
 n
 i




36
How Discriminatory?
37
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.
38
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.
39
Experiments
• Database 2: (NIST 9 Vol. 1 CD 1)
– 1800 images.
– 900 different fingers.
– 832x768
40
Experiments
• MSU-DBI
– Rejected: 100(4%)
• Why?
– Ref point at corner.
– Poor Quality.
(dryness)
• NIST 9
– Rejected: 100 (5.6%)
• Why?
– The same reasons.
41
Genuine and Imposter Probabilities
42
Experiments
43
ROC curve (MSU-DBI)
44
ROC Curve (NIST9)
45
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.
46
Neyman-Pearson Rule
p X1 | wG , p X 2 | wG , pX1 | wI , p X 2 | wI 
p X1 , X 2 | wG   p X1 | wG   pX 2 | wG 
Joint Probs
p X1 , X 2 | wG   p X1 | wG   pX 2 | wG 
Classifica tion :

w
X10 , X 02   G
 wI






p X10 , X 02 | wG
if

0
0
p X1 , X 2 | wI
otherwise
p X1 , X 2 | wG 

1)   pX , X | w 
1
2
I
 must satisfy : 
2)  0  2 pX1 , X 2 | wI dX1dX 2
RG

R 2  RG2  RI2
47
Questions?
48