Fingerprint Recognition
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CPSC 601
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Lecture Plan
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Fingerprint features
Fingerprint matching
2
Fingerprint verification and
identification
3
Coarse representation –Level 1
features
4
Coarse representation –Level 1
features
Left
___ loop
____
Right
____loop
____ ____
Whorl
___ Arch _____ Tented
___ Arch
5
Minutiae –Level 2 features
6
Minutia –Level 2 features
7
Level 3 features
Sweat pores
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Level 3 features
9
Minutiae Detection
Original image
Binary image
Skeleton and
extracted
minutiae
10
Feature extraction process
Fingerprint image
Fingerprint area Ridge pattern &
Frequency image Minutiae points
Orientation image
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Feature extraction process
12
Orientation image of
fingerprint

Computation of gradients over a square-meshed grid of
size 16 x 16; the element length is proportional to its
reliability.
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Orientation image of
fingerprint
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Frequency image
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Ridge frequency: inverse of the average distance
between 2 consecutive peaks
15
Segmentation
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Segmentation is the process of isolating foreground
from background:
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Image block (16x16 pixels) decomposition
Thresholding using variance of gradient for each block
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Why do we need enhancement?
17
Why do we need enhancement?
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Need for Enhancement
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Enhancement
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Initial enhancement may
involve the normalization of
the inherent intensity
variation in a digitized
fingerprint caused either by
the inking or the live-scan
device.
One such process - local area
contrast enhancement (LACE)
is useful to provide such
normalization through the
scaling of local neighborhood
pixels in relation to a
calculated global mean.
(a) An inked fingerprint image
(b) The results of the LACE algorithm
on (a)
Histograms of fingerprint images in
(a) and (b) above.
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Enhancement
Another type of enhancement is contextual filtering that:
1. Provide a low-pass (averaging) effect along the ridge direction with the aim
of linking small gaps and filling impurities due to pores or noise.
2. Perform a bandpass (differentiating) effect in a direction orthogonal to the
ridges to increase the discrimination between ridges and valleys and to
separate parallel linked ridges.
3. Gabor filters have both frequency-selective and orientation-selective
properties and have optimal joint resolution in both spatial and frequency
domains.
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Enhancement
Graphical representation (lateral and top view) of the Gabor filter
defined by the parameters θ = 1350, f = 1/5, σx = σy = 3
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Enhancement
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The simplest and most natural approach for extracting
the local ridge orientation field image, D, containing
elements θij, in a fingerprint image is based on the
computation of gradients in the fingerprint image.
23
Enhancement
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The local ridge frequency (or density) fxy at point [x, y] is
the inverse of the number of ridges per unit length along
a hypothetical segment centered at [x, y] and orthogonal
to the local ridge orientation θxy.
A frequency image F, analogous to the orientation image
D, is defined if the frequency is estimated at discrete
positions and arranged into a matrix. The local ridge
frequency varies across different fingers and regions.
The ridge pattern can be locally modeled as a sinusoidalshaped surface and the variation theorem can be
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exploited to estimate the unknown frequency.
Enhancement
The variation of the function h in the interval [x1, x2] is the sum of
the amplitudes α1, α2, … α8. If the function is periodic or the function
amplitude does not change significantly within the interval of
interest, the average amplitude αm can be used to approximate the
individual α. Then the variation can be expressed as 2αm multiplied
by the number of periods of the function over the interval.
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Gabor filters
26
Enhancement Results
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Artifacts
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Post-processing
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Extraction of minutiae


count the
number
of ridge
_____
___
______
__ pixels
in
the window
except
middle
_____
______
__ ___
______
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Feature extraction errors
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The feature extraction algorithms are imperfect and often
introduce measurement errors
Errors may be made during any of the feature extraction
stages, e.g., estimation of orientation and frequency images,
detection of the number, type, and position of the
singularities and minutiae, segmentation of the fingerprint
area from background, etc.
Aggressive enhancement algorithms may introduce
inconsistent biases that perturb the location and orientation of
the reported minutiae from their gray-scale counterparts
In low-quality fingerprint images, the minutiae extraction
process may introduce a large number of spurious minutiae
and may not be able to detect all the true minutiae
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Fingerprint Recognition
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Fingerprint features
Fingerprint matching
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Intra-variability
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Matching fingerprint images is an extremely difficult
problem, mainly due to the large variability in different
impressions of the same finger (intra-variability). The
main factors are:
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Displacement (global translation of the fingerprint area)
Rotation
Partial overlap
Non-linear distortion:
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the act of sensing maps the three-dimensional shape of a finger
onto the two-dimensional surface of the sensor
skin elasticity
Pressure and skin condition
Noise: introduced by the fingerprint sensing system
Feature extraction errors
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Matching illustration
Examples of mating, non-mating and multiple mating minutiae.
34
Matching illustration
An example of matching the search minutiae set in (a) with the file
minutiae set in (b) is shown in (c).
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Difficulty in fingerprint
matching
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Small overlap
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Non-linear distortion
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Different skin
conditions
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Finger placement
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A finger placement is correct when user:
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Approaches the finger to the sensor through a
movement that is orthogonal to the sensor
surface
Once the finger touches the sensor surface,
the user does not apply traction or torsion
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Non-linear distortion
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Non-linear distortion
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Three distinct regions:
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A close-contact region (a) where the high pressure
and the surface friction do not allow any skin slippage
A transitional region (b) where an elastic distortion is
produced by skin compression and stretching
An external region (c) where the light pressure allows
the finger skin to be dragged by the finger movement
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Fingerprint Matching
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Minutiae-based matching: finding the
alignment between the template and the input
minutiae sets that results in the maximum
number of minutiae pairings
Correlation-based matching: correlation
between corresponding pixels is computed for
different alignments (e.g. various displacements
and rotations)
Ridge feature-based matching: comparison in
term of features such as local orientation and
frequency, ridge shape, texture information, etc.
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Local minutiae matching
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Minutiae correspondence
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Pre-alignment
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Absolute pre-alignment
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The most common absolute pre-alignment technique
translates and rotates the fingerprint according to the
position of the core point and the delta point (if a
delta exists)
Relative pre-alignment
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By superimposing the singularities
By correlating the orientation images
By correlating ridge features (e.g. length and
orientation of the ridges)
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Fingerprint matching with absolute
pre-alignment
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First align the fingerprints
using the global structure.
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Input image
Template image
Extract the core-points
(prominent symmetry points)
to estimate the transformation
parameters v, ϕ (v from the
difference in their position,
and ϕ from the difference in
their angle) by complex
filtering of the smoothed
orientation field.
Then use the local structure
for ”point-to-point” matching.
44
Minutiae matching with
relative pre-alignment
Pre-alignment based
on the minutiae
marked with circles
and the associated
ridges
Matching results,
where paired minutiae
are connected by
green lines
45
Triangular matching
46
Ridge count
47
DT method

We first compute the Delaunay
triangulation of minutiae sets Q and P.
Second, we use triangle edge as
comparing index. To compare two edges,
Length, θ1 , θ2 , Ridgecount values are
used, all of which invariant of the
translation and rotation.
Matching parameters
Lengthinput  Lengthtemplate
max( Lengthinput , Lengthtemplate )
 threshold1
1input  1template  threshold2
| Ridgecountinput  Ridgecounttemplate | threshold3
Correlation based matching
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Non-linear distortion makes fingerprint
impressions significantly different in terms of
global structure; two global fingerprint patterns
cannot be reliably correlated
Due to the cyclic nature of fingerprint patterns,
if two corresponding portions of the same
fingerprint are slightly misaligned, the
correlation value falls sharply
A direct application of 2D correlation is
computationally very expensive
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Example of correlation-based
matching
From: Correlation-Based Fingerprint Matching with
Orientation Field Alignment
Almudena Lindoso, Luis Entrena, Judith Liu-Jimenez, and Enrique San Millan
Ridge feature-based matching
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Most frequently
used features for
fingerprint
matching:
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Orientation image
Singular points
(loop and delta)
Ridge line flow
Gabor filter
responses
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Comparison of Biometric
Technologies
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Fingerprint Recognition
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Strengths
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It is a mature and proven
core technology, capable of
high levels of accuracy
It can be deployed in a
range of environments
It employs ergonomic,
easy-to-use devices
The ability to enroll
multiple fingers can
increase system accuracy
and flexibility
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Weaknesses
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Most devices are unable to
enroll some small
percentage of users
Performance can
deteriorate over time
It is associated with
forensic applications
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References and Links
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Signal Processing Institute, Swiss Federal
Institute of Technology
http://scgwww.epfl.ch/
Biometric Systems Lab, University of
Bologna
http://bias.csr.unibo.it/research/biolab/
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