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Fingerprint Recognition CPSC 601CPSC 601

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Lecture Plan

Fingerprint features Fingerprint matching

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Fingerprint verification and identification

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Coarse representation –Level 1 features

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Coarse representation –Level 1 features

___ ____ ____ ____ ____ ___ _____ ______ ____ ____ ____ ____ ___ _____ ___ Left loop Right loopLeft loop Right loop Whorl Whorl Arch Arch Tented Tented ArchArch

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Minutiae –Level 2 features

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Minutia –Level 2 features

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Level 3 features

Sweat pores

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Level 3 features

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Minutiae Detection

Original image Binary image Skeleton and extracted

minutiae

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Feature extraction process

Fingerprint Fingerprint imageimage

Fingerprint areaFingerprint area

Frequency imageFrequency image

Orientation imageOrientation image

Ridge pattern &Ridge pattern &

Minutiae pointsMinutiae points

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Feature extraction process

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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

____ _________ _______ __ ___ _______ ________ _______ ___________ _____

Ridge frequency: inverse of the average distance between 2 consecutive peaks

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Segmentation Segmentation is the process of isolating

foreground from background: Image block (16x16 pixels) decomposition Thresholding using variance of gradient for each block

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Why do we need enhancement?

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Why do we need enhancement?

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Need for Enhancement

2020

EnhancementEnhancement Initial enhancement may Initial enhancement may

involve the normalization of involve the normalization of the inherent intensity the inherent intensity variation in a digitized variation in a digitized fingerprint caused either by fingerprint caused either by the inking or the live-scan the inking or the live-scan device.device.

One such process - local area One such process - local area contrast enhancement contrast enhancement (LACE) is useful to provide (LACE) is useful to provide such normalization through such normalization through the scaling of local the scaling of local neighborhood pixels in neighborhood pixels in relation to a calculated relation to a calculated global mean.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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EnhancementEnhancementAnother type of enhancement is contextual filtering that:Another type of enhancement is contextual filtering that:1. Provide a low-pass (averaging) effect along the ridge direction 1. Provide a low-pass (averaging) effect along the ridge direction

with the aimwith the aim of linking small gaps and filling impurities due to pores or noise. of linking small gaps and filling impurities due to pores or noise. 2. Perform a bandpass (differentiating) effect in a direction 2. Perform a bandpass (differentiating) effect in a direction

orthogonal to the ridges to increase the discrimination between orthogonal to the ridges to increase the discrimination between ridges and valleys and to separate parallel linked ridges.ridges and valleys and to separate parallel linked ridges.

3. Gabor filters have both frequency-selective and orientation-3. Gabor filters have both frequency-selective and orientation-

selective properties and have optimal joint resolution in both selective properties and have optimal joint resolution in both spatial and frequency domains.spatial and frequency domains.

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EnhancementEnhancement

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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EnhancementEnhancement

The simplest and most natural approach for The simplest and most natural approach for

extracting the local ridge orientation field image, extracting the local ridge orientation field image,

D, containing elements D, containing elements θθijij, in a fingerprint image , in a fingerprint image

is based on the computation of gradients in the is based on the computation of gradients in the

fingerprint image. fingerprint image.

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EnhancementEnhancement

The local ridge frequency (or density) fThe local ridge frequency (or density) fxyxy at point [x, y] is the inverse of the at point [x, y] is the inverse of the

number of ridges per unit length along a hypothetical segment centered at number of ridges per unit length along a hypothetical segment centered at

[x, y] and orthogonal to the local ridge orientation [x, y] and orthogonal to the local ridge orientation θθxyxy. .

A frequency image A frequency image FF, analogous to the orientation image , analogous to the orientation image DD, is defined if the , is defined if the

frequency is estimated at discrete positions and arranged into a matrix. The frequency is estimated at discrete positions and arranged into a matrix. The

local ridge frequency varies across different fingers and regions. local ridge frequency varies across different fingers and regions. The ridge pattern can be locally modeled as a sinusoidal-shaped surface and The ridge pattern can be locally modeled as a sinusoidal-shaped surface and

the variation theorem can be exploited to estimate the unknown frequency.the variation theorem can be exploited to estimate the unknown frequency.

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EnhancementEnhancement

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

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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

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

Fingerprint features Fingerprint matching

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Intra-variability

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: Displacement (global translation of the fingerprint area) Rotation Partial overlap Non-linear distortion:

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 illustrationMatching illustration

Examples of mating, non-mating and multiple mating minutiae.

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An example of matching the search minutiae set in (a) with the file minutiae set in (b) is shown in (c).

Matching illustrationMatching illustration

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Difficulty in fingerprint matching

Small overlap

Non-linear distortion

Different skin conditions

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Finger placement

A finger placement is correct when user: 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

Three distinct regions: 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

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

Absolute pre-alignment 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 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

First align the fingerprints using the global structure.

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.

Input image Template image

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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

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Triangular matching

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Ridge count

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

1),max(threshold

LengthLength

LengthLength

templateinput

templateinput

211 thresholdtemplateinput

3|| thresholdRidgecountRidgecount templateinput

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Correlation based matching

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

Example of correlation-based matching

From: Correlation-Based Fingerprint Matching withOrientation Field AlignmentAlmudena Lindoso, Luis Entrena, Judith Liu-Jimenez, and Enrique San Millan

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Ridge feature-based matching

Most frequently used features for fingerprint matching: 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

Strengths 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

Weaknesses 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 LinksReferences and Links

Signal Processing Institute, Swiss Federal Institute of Technologyhttp://scgwww.epfl.ch/

Biometric Systems Lab, University ofBolognahttp://bias.csr.unibo.it/research/biolab/