Transcript Document

Fingerprint Recognition
Fingerprint recognition is one of the oldest and most
researched fields of biometrics.
Some biological principles (Moenssens 1971) related
to fingerprint recognition are as follows:
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Individual epidermal ridges and furrows have
different characteristics for different fingerprints.
This forms the foundation of fingerprint
recognition
The configuration types are individually variable;
but they vary within limits that allow for a
systematic classification.
Herein lies the basis for fingerprint
classification.
The configuration and minute details of furrows
are permanent and unchanging.
Fingerprint Formation
 Fingerprints are fully formed at about
seven months of fetus development and
finger ridge configurations do not
change throughout the life of an
individual except due to accidents such
as bruises and cuts on the fingertips
(Babler, 1991).
 Unrelated persons of the same race have
very little generic similarity in their
fingerprints.
 Parent and child have some generic
similarity as they share half the genes.
 Siblings have more similarity.
 The maximum generic similarity is
observed in monozygotic (identical)
twins.
Fingerprint Sensors
Fingerprint Sensors
 Optical
 Silicon Based Capacitive Sensors
 Ultrasound
 Thermal
Optical Sensors
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Oldest and most widely used technology.
Majority of companies use optical technology.
The finger is placed on a coated hard plastic plate.
In most devices, a charged coupled device (CCD) converts
the image of the fingerprint, with dark ridges and light
valleys, into a digital signal.
 The brightness is either adjusted automatically or manually,
leading to a usable image.
Optical Sensors-contd..
Advantages
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They are the most proven over time.
They can withstand, to some degree, temperature fluctuations.
They are fairly inexpensive.
They can provide resolutions up to 500 dpi.
Disadvantages
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Size, the sensing plate must be of sufficient size to achieve a quality image
Residual prints from previous users can cause image degradation, as severe
latent prints can cause two sets of prints to be superimposed.
The coating and CCD arrays can wear with age, reducing accuracy.
A large number of vendors of fingerprint sensing equipment are gradually
shifting towards silicon-based technology.
Silicon Based Sensors
• Silicon technology has gained considerable
acceptance since its introduction in the late 90's.
• Most silicon, or chip, technology is based on DC
Capacitance, but some also use AC Capacitance.
• The silicon sensor acts as one plate of a capacitor,
and the finger is the other.
• The capacitance between the sensing plate and the
finger is converted into an 8-bit grayscale digital
image.
Silicon Based Sensors-contd..
• Fingerprint cards contain numerous capacitive plates which measure the
capacitance between the plates and the fingertip.
• When the finger is placed on the sensor extremely weak electrical
charges are created, building a pattern between the finger's ridges or
valleys and the sensor's plates.
• Using these charges the sensor measures the capacitance pattern across
the surface.
• The measured values are digitized by the sensor then sent to the
neighboring microprocessor.
• This can be done directly by applying an electrical charge to the plate or
by using electronic pulses passed to the fingertip.
Direct v/s Active Capacitive Measurement
Silicon Based Sensors-contd..
Advantages
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The Silicon chip comprises of about 200*200 lines on a wafer the size of
1cm*1.5cm, thus providing a pretty good resolution for the image.
Hence, Silicon generally produces better image quality, with less surface area,
than optical.
Also, the reduced size of the chip means lower costs especially with the
dropping costs in Silicon chip manufacturing.
Miniaturization of Silicon chips also makes it possible for the chips to be
integrated into numerous devices.
Disadvantages
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In spite of claims by manufacturers that Silicon is much more durable than
optical, Silicon's durability, especially in sub-optimal conditions, has yet to be
proven.
Also, with the reduction in sensor size, it is even more important to ensure that
enrolment and verification are done carefully.
Ultrasound Sensors
• Ultrasound technology is perhaps the most
accurate of the fingerprint technologies.
• It uses transmitted ultrasound waves and
measures the distance based on the
impedance of the finger, the plate, and air.
• Preliminary usage of products indicates that
this is a technology with significant promise.
Ultrasound Sensors-contd..
Advantages
 Ultrasound is capable of penetrating dirt and residue on the sensing plate and the
finger.
 This overcomes the drawbacks of optical devices which can't make that
distinction.
 It combines a strength of optical technology-large platen size and ease of use,
with a strength of silicon technology-the ability to overcome sub-optimal reading
conditions.
 It is also virtually impossible to deceive an ultrasound system.
Disadvantages
 The quality of the image depends to a great extent on the contact between the
finger and the sensor plate which could also be quite hot.
Thermal Sensors
• Uses Pyro Electric material.
• Pyro-electric material is able to convert a difference
in temperature into a specific voltage.
• This effect is quite large, and is used in infrared
cameras.
• A thermal fingerprint sensor based on this material
measures the temperature differential between the
sensor pixels that are in contact with the ridges and
those under the valleys, that are not in contact.
Thermal Sensors-contd..
Advantages
• A strong immunity to electrostatic discharge
• Thermal imaging functions as well in extreme temperature conditions as at room
temperature.
• It is almost impossible to deceive with artificial fingertips.
Disadvantages
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A disadvantage of the thermal technique is that the image disappears quickly.
When a finger is placed on the sensor, initially there is a big difference in
temperature, and therefore a signal, but after a short period (less than a tenth of a
second), the image vanishes because the finger and the pixel array have reached
thermal equilibrium.
However, this can be avoided by using a scanning method where the finger is
scanned across the sensor which is the same width as the image to be obtained ,
but only a few pixels high.
Fingerprint Classification
Whorl
Right Loop Left Loop
Tented Arch
Arch
Classification of Fingerprints
•Large volumes of fingerprints are being collected in everyday applications-for e.g.. The FBI database has 70
million of them.
•To reduce the search time and computational complexity classification is necessary.
•This allows matching of fingerprints to only a subset of those in the database.
•An input fingerprint is first matched at a coarse level to one of the pre-specified types and then, at a finer level,
it is compared to the subset of the database containing that type of fingerprints only.
•Numerous algorithms have been developed in this direction.
Line Types Classification
Bifurcation: It is the intersection of two or more line-types which converge or diverge.
Arch: They are found in most patterns, fingerprints made up primarily of them are called “Arch Prints”.
Loop: A recursive line-type that enters and leaves from the same side of the fingerprint.
Island: A line-type that stands alone.( i.e. does not touch another line-type)
Ellipse: A circular or oval shaped line-type which is generally found in the center of the fingerprint, it is
generally found in the Whorl print pattern.
Tented Arch: It quickly rises and falls at a steep angle. They are associated with “Tented Arch Prints”.
Spiral: They spiral out from the center and are generally associated with “Whorl Prints”.
Rod: It generally forms a straight line. It has little or no recurve feature. They are gennerally found in the
center.
Sweat Gland: The moisture and oils they produce actually allow the fingerprint to be electronically imaged.
Automatic Verification System
Feature Extraction
 The human fingerprint is comprised of various types of ridge
patterns.
 Traditionally classified according to the decades-old Henry system:
left loop, right loop, arch, whorl, and tented arch.
 Loops make up nearly 2/3 of all fingerprints, whorls are nearly 1/3,
and perhaps 5-10% are arches.
 These classifications are relevant in many large-scale forensic
applications, but are rarely used in biometric authentication.
Feature Enhancement
Original
Enhanced
 The first step is to obtain a clear image of the fingerprint.
 Enhancement is carried out so as to improve the clarity of ridge and furrow structures of
input fingerprint images based on the estimated local ridge orientation and frequency.
 For grayscale images, areas lighter than a particular threshold are discarded, and those
darker are made black.
 The ridges are then thinned from 5-8 pixels in width down to one pixel, for precise
location of endings and bifurcations.
Feature Extraction-contd..
• Minutiae localization is the next step.
•Even a very precise image has distortions and false minutiae that need to be filtered out. (e.g.
search and eliminate one of two adjacent minutiae)
•Anomalies caused by scars, sweat, or dirt appear as false minutiae, and algorithms locate
any points or patterns that don't make sense, such as a spur on an island (probably false) or a
ridge crossing perpendicular to 2-3 others (probably a scar or dirt).
•A large percentage of would-be minutiae are discarded in this process.
• The point at which a ridge ends, and the point where a bifurcation begins, are the most
rudimentary minutiae. Once the point has been situated, its location is commonly indicated by
the distance from the core, with the core serving as the 0,0 on an X,Y-axis. In addition to the
placement of the minutia, the angle of the minutia is normally used. When a ridge ends, its
direction at the point of termination establishes the angle. This angle is taken from a horizontal
line extending rightward from the core, and can be up to 359.
• In addition to using the location and angle of minutiae, some classify minutia by type and
quality. The advantage of this is that searches can be quicker, as a particularly notable
minutia may be distinctive enough to lead to a match. [6]
Template Selection
•The matching accuracy of a biometrics-based authentication system relies on the stability
(permanence) of the biometric data associated with an individual over time.
•The biometric data acquired from an individual is susceptible to changes introduced due to
improper interaction with the sensor (e.g., partial fingerprints), modifications in sensor
characteristics (e.g., optical vs. solid-state fingerprint sensor), variations in environmental
factors (e.g.,dry weather resulting in faint fingerprints) and temporary alterations in the
biometric trait itself (e.g., cuts/scars on fingerprints).
•Thus, it is possible for the stored template data to be significantly different from those obtained
during authentication, resulting in an inferior performance (higher false rejects) of the biometric
system. [9]
Variation in fingerprint exhibiting partial overlap.
Template Selectioncontd..(Solutions to variations)
•Multiple templates, that best represent the variability associated with a user's
biometric data, should be stored in the database. (E.g. One could store multiple
impressions pertaining to different portions of a user's fingerprint in order to
deal with the problem of partially overlapping fingerprints.)
• There is a tradeoff between the number of templates, and the storage and
computational overheads introduced by multiple templates.
•For an efficient functioning of a biometric system, this selection of templates should
be done automatically.
•There are two methods that are discussed in the literature. Please refer to references
[9] for further details.
Matching Algorithm
•Automatic Minutiae Detection: Minutiae are essentially terminations and
bifurcations of the ridge lines that constitute a fingerprint pattern.
•Automatic minutiae detection is an extremely critical process, especially in lowquality fingerprints where noise and contrast deficiency can originate pixel
configurations similar to minutiae or hide real minutiae.
Algorithm:
•The basic idea here is to compare the minutiae on the
two images.
•The figure alongside is the input given to the system,
as can be seen from the figure the various details of
this image can be easily detected. Hence, we are in a
position to apply the AMD algorithm.
Matching Algorithm-contd..
Algorithm (contd.)
• The next step in the algorithm is to mark all
the minutiae points on the duplicate image of
the input fingerprint with the lines much
clear after feature extraction.
• Then this image is superimposed onto the
input image with marked minutiae points as
shown in the figure.
• Finally a comparison is made with the
images in the database and a probabilistic
result is given.
Problems With AMD
• It is difficult to extract the minutiae points accurately
when the fingerprint is of low quality.
•This method does not take into account the global
pattern of ridges and furrows.
• Fingerprint matching based on minutiae has problems
in matching different sized (unregistered) minutiae
patterns.
FX3 Algorithm [2]
•FX3 sdk is a collection of innovative algorithms for the processing, feature
extraction and matching of fingerprints which provides great security and
efficiency.
•FX3 implements different matching stages (multi-modal matching) and
performs feature extraction, directly on the gray-scale images.
Accuracy
• FAR - False Accept Probability that an impostor is wrongly accepted by the system.
• FRR - False Reject Rate Probability that an authorized user is wrongly rejected by the
system.
• EER - Defined as the threshold value where the FAR and FRR are equal.
• Lower EER means better performance.
Existing System:
0.01% FAR & 1% FRR (depends on evaluation scheme)
Research Issues
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Some of the research issues are related to security of the fingerprint
recognition system, while some are related to improving the general
system so that we get a better FAR & FRR.
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The research topics that we have covered in our presentation are:
1) Multibiometrics System.
2) Security against Fake fingerprints.
3) Third Level Detail.
Multibiometrics Systems
• Multibiometric systems as the name implies use multiple biometric traits.
• Multibiometric systems, are expected to be more reliable.
• Multibiometric systems address the problem of non-universality, since multiple
traits can ensure sufficient population coverage.
• Multibiometric systems provide anti-spoofing measures by making it difficult for an
intruder to simultaneously spoof the multiple biometric traits of a legitimate user.
• By asking the user to present a random subset of biometric traits, the system ensures
that a “live” user is indeed present at the point of data acquisition. Thus, a challengeresponse type of authentication can be facilitated using multibiometric systems.[12]
Attacks
Artificially
created
Biometrics
Attack at
the
Database
Attacking
Via Input
Port
Attacks-contd..
Spoofing:- “The process of defeating a biometric system through the introduction
of fake biometric samples”. Examples of spoof attacks on a fingerprint recognition
system are lifted latent fingerprints and artificial fingers.
Examples of spoofed fingers.
•Put subject’s finger in impression material and create a mold.
•Molds can also be created from latent fingerprints by photographic etching
techniques like those used in making of PCB (gummy fingers).
•Use play-doh, gelatin, or other suitable material to cast a fake finger.
•Worst-case scenario: dead fingers.[7]
Attacks-solutions..
Hardware Solution
•Temperature sensing, detection of pulsation on fingertip, pulse oximetry, electrical
conductivity, ECG, etc.
Software Solution (Research going on)
•Live fingers as opposed to spoofed or cadaverous fingers show some kind of moisture pattern
due to perspiration.
•The main idea behind this method is to take two prints after a time frame of say 5 seconds and
the algorithm makes a final decision based on the vitality of the fingerprint. [7]
Third Level Detail
 This is the newest approach under research towards
fingerprint recognition.
 Here the expert is not specifically analyzing the
fingerprint characteristics, rather they are studying the
pores and the outlines of the fingerprint ridges.
 The above fingerprint has been developed on clear plastic with cyanoacrylate
fuming.
 The level of third level detail that can be recovered is very dependant on the
chemical treatments used and the subsequent quality of the mark.
 If for example the fingerprint has been stained with Basic Yellow the dye
often obscures the pore detail.
Third Level Detail-contd..
 To maximize the quality of the fingerprint
the image was lit from behind the
baseboard as seen in the diagram
alongside:-
• Once the fingerprint had been acquired it is placed into the ‘digital darkroom’
(Image Pro Plus) for processing. Then the following steps are carried out:1. Application of a sobel filter.
2. The image is inverted.
3. Thresholding is applied to the image to remove some of the grey scale values.
The fingerprint is now ready for analysis and can be printed at any size the user
requires.[11]
Applications
•Banking Security - ATM security,card transaction
•Physical Access Control (e.g. Airport)
•Information System Security
•National ID Systems
•Passport control (INSPASS)
•Prisoner, prison visitors, inmate control
•Voting
•Identification of Criminals
•Identification of missing children
•Secure E-Commerce (Still under research)
Biometric Comparison
Latest Technologies
 Fingerprint Registry Service-Lockheed Martin [10]

The Fingerprint Registry Service is a low-investment approach to state-ofthe-art fingerprint technology.
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Technology needed for civil, commercial and volunteer organizations to
screen individuals using modern fingerprint technology is expensive.

The Lockheed Martin Fingerprint Registry Service Center was opened in
August ‘98 in Orlando, FL.

The center provides affordable, centralized fingerprint processing and
database management services to volunteer organizations, financial
institutions, schools and service agencies at the national, state, and local
levels.
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Provides fingerprint technology that will be very effective at screening
applicants for sensitive jobs and for identifying individuals with undesirable
histories, regardless of alias.
Latest Technologies-contd..
 Compaq Fingerprint Identification Technology

The first affordable biometric security technology
offering.

Compatible with Compaq DeskPro, Armada PCs, and
Professional Workstations.

Compatible with Microsoft Windows 95 and Windows
NT Workstation 4.0 operating systems.

Dramatically improves the security of Microsoft
Windows NT based networks by effectively replacing
passwords with unique fingerprints.

Uses Identicator’s reader technology and it’s software
algorithm technology.

The fingerprint reader is compatible and complimentary
to all smart card based systems.
1)
Biometric systems lab - http://bias.csr.unibo.it/research/biolab/bio_tree.html
2)
Biometrica - http://www.biometrika.it/eng/wp_fx3.html
3)
International Biometric Group – http://www.biometricgroup.com/reports/public/ reports/finger-scan_extraction.html
4)
Dr. Dirk Scheuermann - “http://www.darmstadt.gmd.de/~scheuerm/lexikon/vlta_eng.html”
5)
Handbook of fingerprint recognition - D. Maltoni, D. Maio, A. K. Jain, S. Prabahakar - Springer – 2003
6)
BiometricsInfo.org - http://www.biometricsinfo.org/fingerprintrecognition.htm
7)
“Issues for liveliness detection in Biometrics” - Stephanie Schuckers, Larry Hornak,Tim Norman, Reza Derakhshani,
Sujan Parthasaradhi
8)
“Overview of Biometrics & Fingerprint Technology” - Dr. Y.S. Moon
9)
“Biometric Template Selection: A Case Study in Fingerprints” - Anil Jain, Umut Uludag and Arun Ross
http://biometrics.cse.msu.edu/JainUludagRoss_AVBPA_03.pdf
10)
Fingerprint Registry Service - http://www.lockheedmartin.com/lmis/level4/frs.html
11)
Rideology and Poroscopy - http://www.eneate.freeserve.co.uk/thirdlevel.PDF
12)
Multibiometric Systems - Anil K. Jain and Arun Ross
http://biometrics.cse.msu.edu/RossMultibiometric_CACM04.pdf111