Fingerprint Recognition

1
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
• 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.

2
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.

3
Fingerprint Sensors
4
Fingerprint Sensors

• Optical
• Silicon Based Capacitive Sensors
• Ultrasound
• Thermal

5
Optical Sensors

• 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.

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Optical Sensors-contd..

• Advantages
• 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
• 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.

7
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.

8
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.

9
Direct v/s Active Capacitive Measurement
10
Silicon Based Sensors-contd..

• Advantages
• The Silicon chip comprises of about 200200 lines
  on a wafer the size of 1cm1.5cm, 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
• 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.

11
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.

12
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.

13
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.

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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
• 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.

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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.

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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.
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Automatic Verification System
18
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.

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Feature Enhancement
Enhanced
Original

• 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.

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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

21
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.
22
Template Selection-contd..(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.

23
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 low-quality
  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.

24
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.

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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.

26
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.

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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)

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Research Issues

• 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.
• The research topics that we have covered in our
  presentation are
• 1) Multibiometrics System.
• 2) Security against Fake fingerprints.
• 3) Third Level Detail.

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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 challenge-response type
  of authentication can be facilitated using
  multibiometric systems.12

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Attacks
Artificially created Biometrics
Attack at the Database
Attacking Via Input Port
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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 subjects 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

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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

Live
Dead
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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.

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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

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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)

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Biometric Comparison
37
Latest Technologies

• Fingerprint Registry Service-Lockheed Martin 10
• The Fingerprint Registry Service is a
  low-investment approach to state-of-the-art
  fingerprint technology.
• 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.
• Provides fingerprint technology that will be very
  effective at screening applicants for sensitive
  jobs and for identifying individuals with
  undesirable histories, regardless of alias.

38
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 Identicators reader technology and its
  software algorithm technology.
• The fingerprint reader is compatible and
  complimentary to all smart card based systems.

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References

1. Biometric systems lab - http//bias.csr.unibo.it/r
   esearch/biolab/bio_tree.html
2. Biometrica - http//www.biometrika.it/eng/wp_fx3.h
   tml
3. International Biometric Group
   http//www.biometricgroup.com/reports/public/
   reports/finger-scan_extraction.html
4. Dr. Dirk Scheuermann - http//www.darmstadt.gmd.d
   e/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.lockheed
    martin.com/lmis/level4/frs.html
11. Rideology and Poroscopy - http//www.eneate.freese
    rve.co.uk/thirdlevel.PDF
12. Multibiometric Systems - Anil K. Jain and Arun
    Ross http//biometrics.cse.msu.edu/RossMultibiomet
    ric_CACM04.pdf111

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Thank You!!