Introduction to Biometrics

1
Introduction to Biometrics

• Dr. Bhavani Thuraisingham
• The University of Texas at Dallas
• Lecture 7
• Biometric Technologies Finger Scan
• September 14, 2005

2
Outline

• Introduction
• Basic Terms
• Technologies
• Finger Scan Process
• Feature Extraction
• Classification
• Accuracy and Integrity
• Biometric Middleware
• Strengths and Weaknesses
• Biometric vs. Non Biometric Fingerprinting
• Research Directions
• Project Related Information

3
References

• Course Text Book, Chapter 4
• http//www.biometricsinfo.org/fingerprintrecogniti
  on.htm

4
Introduction

• What is Finger-Print Scanning
• Fingerprint scanning is the acquisition and
  recognition of a persons fingerprint
  characteristics for identification purposes.
• This allows the recognition of a person through
  quantifiable physiological characteristics that
  verify the identity of an individual.
• Methods
• There are basically two different types of
  finger-scanning technology that make this
  possible.
• One is an optical method, which starts with a
  visual image of a finger.
• The other uses a semiconductor-generated electric
  field to image a finger.

5
Introduction (Concluded)

• There are a range of ways to identify
  fingerprints.
• traditional police methods of matching minutiae
• straight pattern matching
• Ultrasonics
• Fingerprint revenues are projected to grow from
  144.2m in 2002 to 1,229.8m in 2007. Fingerprint
  revenues are expected to comprise approximately
  30 of the entire biometric technologies
• Applications
• to access networks and PCs, enter restricted
  areas, and to authorize transactions.
• Deployed in many locations (discussed in text
  book)

6
Basic Terms

• Components
• Image acquisition systems, image processing
  components, template generation and matching
  components, storage components
• Surface on which finger is placed is Platen or
  Scanner
• Finger scan module
• consists of platen printed circuit board
  standard connector that transmits digitized
  information to a peripheral or standalone device

7
Example Technologies

• Optical Technology
• Oldest technology
• Camera registers the image of the fingerprint
  against a coated glass or plastic platen
• Black, gray and white lines
• Silicon Technology
• Silicon chip embedded in a platen
• High image quality
• Commercially available since around 1998
• Ultrasound Technology
• Transmit acoustic waves to the finger and
  generating images

8
Process

• Image Acquisition
• Measured in terms of dots per inch
• Center of the finger print must be placed on the
  platen
• Need appropriate size for platen
• Image Processing
• Eliminate gray areas from image
• Convent gray pixels to black and white pixels
• Location of Distinctive Characteristics
• Fingerprints consists of ridges and valleys
• Swirls, loops, arches, deltas
• Ridges and valleys are characterized by
  irregularities called minutiae
• A finger scan image can produce about 15-50
  minutiae

9
Process (Concluded)

• Template Creation
• Vendors use proprietary algorithms
• Depends on the following
• Location and angle of a minutiae point
• Distance and position of minutiae relative to the
  core
• Type and quality of the minutiae
• Need to eliminate sweat, scars, dirt, etc.
• Template matching
• May depend on the number of minutiae matched

10
Methods of Finger PrintingMinutiae vs. Pattern
matching

• Minutiae
• Most of the finger-scan technologies are based on
  minutiae
• Pattern Matching
• Feature extraction and template generation based
  on series of ridges as opposed to discrete points
• Advantage Minutiae points affected by wear and
  tear
• Disadvantage Sensitive to proper placement of
  finger large storage for templates
• Correlation
• Michigan State University of developing
  correlation based methods

11
Feature Extraction

• The human fingerprint is comprised of various
  types of ridge patterns
• left loop, right loop, arch, whirl, and tented
  arch.
• Loops make up nearly 2/3 of all fingerprints
• whirls are nearly 1/3
• 5-10 are arches.
• Figure 1
• Source Book, URL

12
Feature Extraction (Continued)

• Minutiae (Figure 1), the discontinuities that
  interrupt the otherwise smooth flow of ridges,
  are the basis for most fingerprint
  authentication.
• Many types of minutiae exist, including dots
  (very small ridges), islands (ridges slightly
  longer than dots), ponds or lakes - - - -
• The core is the inner point, normally in the
  middle of the print, around which swirls, loops,
  or arches center.
• Deltas are the points, normally at the lower left
  and right hand of the fingerprint, around which a
  triangular series of ridges center.
• The ridges are also marked by pores, which appear
  at steady intervals.

13
Feature Extraction (Continued)

• Once a high-quality image is captured, there are
  a several steps required to convert its
  distinctive features into a compact template.
• This process, known as feature extraction, is at
  the core of fingerprint technology.
• fingerprint vendor has a proprietary feature
  extraction mechanism
• The image must then be converted to a usable
  format.
• If the image is grayscale, 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.

14
Feature Extraction (Continued)

• Minutiae localization begins with this processed
  image.
• At this point, even a very precise image will
  have distortions and false minutiae that need to
  be filtered out
• an algorithm may search the image and eliminate
  one of two adjacent minutiae, as minutiae are
  very rarely adjacent.
• Anomalies caused by scars, sweat, or dirt appear
  as false minutiae, and algorithms locate any
  points or patterns that do not make sense
• A large percentage of would-be minutiae are
  discarded in this process.

15
Feature Extraction (Concluded)

• The point at which a ridge ends, and the point
  where a bifurcation begins, are the most
  rudimentary minutiae, and are used in most
  applications.
• There is variance in how exactly to situate a
  minutia point
• whether to place it directly on the end of the
  ridge, one pixel away from the ending, or one
  pixel within the ridge ending
• 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.
• Some vendors classify minutia by type and
  quality. The advantage of this is that searches
  can be quicker

16
Fingerprint Classification

• Large volumes of fingerprints are collected and
  stored everyday in a wide range of applications
  including forensics, access control, and driver
  license registration.
• An automatic recognition of people based on
  fingerprints requires that the input fingerprint
  be matched with a large number of fingerprints in
  a database (FBI database contains approximately
  70 million fingerprints).
• To reduce the search time and computational
  complexity, it is desirable to classify these
  fingerprints in an accurate and consistent manner
  so that the input fingerprint is required to be
  matched only with a subset of the fingerprints in
  the database.

17
Fingerprint Classification (Continued)

• Fingerprint classification is a technique to
  assign a fingerprint into one of the several
  pre-specified types already established in the
  literature which can provide an indexing
  mechanism.
• Fingerprint classification can be viewed as a
  coarse level matching of the fingerprints.
• 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.

18
Fingerprint Classification (Concluded)

• Michigan State University has developed an
  algorithm to classify fingerprints into five
  classes,
• whirl, right loop, left loop, arch, and tented
  arch.
• The algorithm separates the number of ridges
  present in four directions (0 degree, 45 degree,
  90 degree, and 135 degree) by filtering the
  central part of a fingerprint with a bank of
  Gabor filters.
• This information is quantized to generate a
  FingerCode which is used for classification.
• Classification is based on a two-stage classifier
  which uses a K-nearest neighbor classifier in the
  first stage and a set of neural networks in the
  second stage.
• The classifier is tested on 4,000 images in the
  NIST-4 database with about 90 accuracy

19
Image Enhancement

• A critical step in automatic fingerprint matching
  is to automatically and reliably extract minutiae
  from the input fingerprint images.
• However, the performance of a minutiae extraction
  algorithm relies heavily on the quality of the
  input fingerprint images.
• In order to ensure that the performance of an
  automatic fingerprint identification/verification
  system will be robust with respect to the quality
  of the fingerprint images, it is essential to
  incorporate a fingerprint enhancement algorithm
  in the minutiae extraction module.
• Michigan State University has developed
  algorithms for enhancement

20
Image Enhancement (Concluded)
Source URL
21
Accuracy and Integrity

• In most cases, false negatives (a failure to
  recognize a legitimate user) are more likely than
  false positives.
• Overcoming a fingerprint system by presenting it
  with a "false or fake" fingerprint will be
  difficult
• Sensors on the market use a variety of means to
  circumvent the problems.
• Problem Someone may attempt to use latent print
  residue on the sensor just after a legitimate
  user accesses the system Presenting a finger to
  the system that is no longer connected to its
  owner.
• Solutions Sensors attempt to determine whether a
  finger is live, and not made of latex Detectors
  for temperature, blood-oxygen level, pulse, blood
  flow, humidity, or skin conductivity would be
  integrated.

22
Biometric Middleware

• Enables various biometric technologies
• Allows match / no-match decisions made by core
  technologies to provide authentication to various
  applications
• Similar to the concept of middleware in systems
• Integrates applications and resources
• Flexible middleware
• Solutions adapted for applications

23
Biometric Middleware (Concluded)
Immigration Entry
Building Entry
PC Entry
Middleware Applications
Middleware
Middleware Services
Iris-scan
Face-scan
Finger-scan
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Strengths and Weaknesses

• Strengths
• Proven technology and high level of accuracy
• Many deployments
• Easy to use
• Can enroll multiple fingers
• Weaknesses
• Some users do not have clear fingerprints
• Over time image quality deteriorates
• Privacy concerns

25
Biometric vs. Non Biometric Fingerprinting

• Fingerprinting, is the acquisition and storage of
  the image of the fingerprint.
• Two types of systems
• Forensic (AFIS Automatic Fingerprint
  Identification System)
• Biometric system
• AFIS stores images of fingerprints need large
  amount if storage.
• Biometric systems store particular data about the
  fingerprint in a much smaller template.
• After the data is extracted, the fingerprint is
  not stored
• The full fingerprint cannot be reconstructed from
  the fingerprint template.
• Used to log on to PC

26
Biometric vs. Non Biometric Fingerprinting
(Concluded)

• Response time - AFIS systems may take hours to
  match a candidate, while fingerprint systems
  respond within seconds
• Cost - an AFIS capture device is very expensive. 
  A PC peripheral fingerprint device is much
  cheaper
• Accuracy - an AFIS system might return the top 5
  candidates with the intent of locating or
  questioning the top suspects. Fingerprint systems
  are designed to return a single yes/no answer.
• Scale AFIS systems scalable to thousands and
  millions of users. Fingerprint systems are and do
  not require significant processing power.
• Capture AFIS systems are designed to use the
  entire fingerprint. Fingerprint systems use only
  the center of the fingerprint.
• Storage AFIS systems need large storage.
  Fingerprint systems do not
• Infrastructure AFIS systems require a backend
  infrastructure for storage, matching, and
  duplicate resolution. Fingerprint systems rely on
  a PC or a peripheral device for processing and
  storage.

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Some Research Directions

• New Biometric Technologies
• Less False Positives and False Negatives
• Better Performance
• Secure Biometrics
• Privacy
• Societal Impact

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Summary

• Most popular biometric technology
• Fairly high accuracy
• Market expected to grow a great deal
• Feature extraction is the key mechanism
• Minutiae based and non Minutiae based methods for
  Biometric matching
• Differences between systems used for forensic
  applications and biometric systems

29
Some Project Related Information

• Dr. Bhavani Thuraisingham
• The University of Texas at Dallas
• Graduate Student Pallabi Parveen
• September 14, 2005

30
TA Office Hours

• Nathalie Tsybulnik
• 7-10pm Monday in ECSS 3.403
• Tuesday from 10.00-11.00am she will usually be
  in the general lab downstairs.

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Some Tools for Project

• http//java.sun.com/products/java-media/jai/forDev
  elopers/jaifaq.htmlwhat
• Java Advanced Imaging Toolkit (product of Sun
  Microsystems)
• Can Download
• http//www.mathworks.com/products/image/
• Matlab Image Processing
• Matlab available in some of our labs
• Cannot download
• CMU Voice Recognition Open Source System Sphinx
• http//cmusphinx.sourceforge.net/html/cmusphinx.ph
  p

32
Face Recognition

• Given at CMU, involves face recognition using
  neural networks.
• 32 images of each of 20 students in the class
  were taken with a variety of head positions and
  facial expressions.
• These images were then used to train and test
  neural networks to recognize individual people,
  and to recognize different face
• Source
• http//www.cs.cmu.edu/afs/cs.cmu.edu/user/avrim/ww
  w/ML94/face_homework.html

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Finger Print Recognition

• Fingerprint Minutiae from Latent and Matching
  Tenprint Images
• NIST Special Database 27 contains latent
  fingerprints from crime scenes and their matching
  rolled fingerprint mates.
• Source http//www.nist.gov/srd/nistsd27.htm
• http//www.itl.nist.gov/iad/894.03/databases/defs/
  dbases.htmlfinglist\

34
Fingerprint Research

• NIST 8-Bit Gray Scale Images of Fingerprint Image
  Groups (FIGS)
• 2000 8-bit gray scale fingerprint image pairs
  including classifications
• 400 fingerprint pairs from each of the five
  classifications - Arch, Left and Right Loops,
  Tented Arch, Whirl)
• Source http//www.nist.gov/srd/nistsd4.htm

35
Iris Recognition

• CASIA Iris Image Database( ver 1.0) includes 756
  iris images from 108 eyes (hence 108 classes).
• For each eye, 7 images are captured in two
  sessions, where three samples are collected in
  the first session and four in the second session.
• Source http//www.sinobiometrics.com/casia20iris
  .htm

36
Keystroke Dynamics as a Biometric for
Authentication

• An emerging non-static biometric technique that
  aims to identify users based on analyzing
  habitual rhythm patterns in the way they type
  Fabian Monrose et al..
• Source http//www.cs.jhu.edu/fabian/papers/fgcs.
  pdf