Biometrics: Personal Identification

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Biometrics Personal Identification
BIOM 426 Biometrics Systems
Instructor Natalia Schmid
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Outline

• Introduction
• Applications
• Identification methods
• Requirements to biometrics
• Biometrics technology
• Automatic Identification
• design
• representation
• feature extraction
• matching
• evaluation
• Privacy Issues

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Introduction

• Identification associating identity with an
  individual.
• Two types of identification problems
• verification (confirming or denying person's
  identity) Am I who I claim I am?
• identification or recognition (establishing
  identity) Who am I?

password
PIN
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Introduction
Facts

• Master Card estimated fraud at 450 million per
  year
• 1 billion dollars worth of calls are made by
  cellular bandwidth thieves
• ATM related fraud - 3 billion annually
• 3,000 illegal immigrants crossing the Mexican
  border each day

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Identification methods
Persons identity is everything what person
represents and believes.
Engineering approach reduce the problem to
(i) some possession ("something what he has") or
(ii) some knowledge ("something what he knows")
Another approach reduce it to a problem of
authentication based on physical characteristics
(physiological or behavioral). Definition
Biometrics are person's identification based on
his/her physiological or behavioral
characteristics. "something that you are"
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New Definition
Biometrics are automated methods of recognizing a
person based on a physiological or behavioral
characteristic.
(BCC2003)
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Requirements to biometrics
1. universality everyone should have it 2.
uniqueness small probability that two persons
are the same in terms of this
characteristic 3. permanence invariance with
the time 4. collectability can be measured
quantitatively 5. performance high
identification accuracy 6. acceptability
acceptance by people 7. circumvention how easy
to fool the system by fraudulent technique
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Accepted Biometrics
Accepted and studied biometrics voice, hand
geometry, gait, fingerprint, ear, face, iris,
retina, fingerprint, infrared facial and hand
vein thermograms, key stroke, signature, DNA
DNA, signature, and fingerprint are recognized
in court of law
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Biometric Technology Overview
Fingerprints are graphical flow-like ridges.
Their formation depends on embrionic
development. Factors (i) genetic,
(ii) environmental Fingerprint acquisition
(i) scanning inked impression, (ii) life-scan
Major representations image, ridges,
minutia (features derived from ridges), or pores

Basic approaches to identification (i)
correlation based (ii) global ridge patterns
(classes) (iii) ridge patterns (iv) fingerprint
minutiae (ridge endings and bifurcations)
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Biometric Technology Overview
Face one of the most acceptable biometrics Two
identification approaches (i) transform
(eigenvalues, analysis of covariance
matrix, orthonormal basis vectors) (ii)
attribute-based approach (geometirc
features) Factors that influence recognition
(i) facial disguise (ii) facial
expressions (iii) lighting conditions (iv)
pose variation
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Biometric Technology Overview
Iris is one of the most reliable biometrics.
Frontal images are obtained using near infrared
camera (320 x 480 pixels) at distance lt 1 meter.
Iris images are (i) segmented and
(ii) encoded. Twins have different iris
patterns.
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Biometric Technology Overview
Voice is a behavioral characteristic and is not
sufficiently unique (large database).
Processing signal subdivided into a few
frequency bands. The most commonly used feature
is cepstral feature (log of FT in each band).
Matching strategies hidden Markoff, vector
quantization, etc. Types of verification
text-dependent text-independent
language-independent.
Voice print is highly accepted biometrics.
Used for identification over the telephone.
Easy to fool the system.
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Biometric Technology Overview
Infrared Facial and Hand Vein Thermogram
Human bodies radiate heat. Infrared sensors
acquire an image of heat distribution along the
body. Images thermograms. Imaging methods
similar to visible spectrum photographs.
Processing raw images are normalized with
respect to heat radiating from landmark features.
In uncontrolled environment, other sources of
heat could be disturbance.
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Biometric Technology Overview
Gait is the specific way one walks. Complex
spatio-temporal behavioral characteristic.
Gait is not unique and does not stay
invariant over time. It is influences by
distribution of body weight, injuries involving
joints or brain, aging. Gait features are
derived from a video sequence and consists of
charactertization of several movements (computer
vision problem).
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Biometric Technology Overview
Retinal Scan Retinal vasculature is rich in
structure. Unique characteristic of each
individual and each eye. Not easy to change or
replicate.
Image capture requires person to peep into an
eye-piece and focus on a specific spot. A
predetermined part of retinal vasculature is
imaged. Requires cooperation. Not accepted by
public. Can reveal some medical conditions as
hypertension.
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Biometric Technology Overview
Signature the way person signs his/her name.
Highly acceptable behavioral biometrics.
Evolves over time and depends on physical and
mental conditions. Easily forged.
Modeling the invariance and automating signature
recognition process is challenging. Two
approaches to signature verification (i)
static (geometric features strokes) (ii)
dynamic (strokes and acceleration, velocity,
trajectory)
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Biometric Technology Overview
Hand and finger geometry is used for access
control (50 of market). System captures
frontal and side views of palm. Measurements
length and width of fingers, various distances.
The representation requirements are only 9
bytes. Hand geometry is not unique but highly
acceptable.
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Comparison of Biometrics Technologies
From Biometrics Personal Identification in
Networked Society, p. 16
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Automatic Identification
History


Prehistoric Chinese used thumb-

print for identification

Alphonse Bertillons System of
Anthropometric Identification (1882)
is based on bodily measuments,
physical description, and photographs.

Henrys fingeprint classification
1685
system (1880) classifies in gt 100 classes.
Sets of
rules
are developed for
(i) matching of biometrics
(ii) searching databases
Automatic identification
is due to inexpensive computer resources,
advances in
computer vision, pattern recognition, and image
understanding.
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Applications

• Civil applications
• Banking (electronic funds transfer, ATM security,
  Internet commerce, credit card transactions)
• Physical access control (airport)
• Information system security (access to databases
  via login)
• Customs and immigration (identification based on
  hand geometry)
• Voter/driver registration
• Telecommunications (cellular bandwidth access
  control)

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Example
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Automatic Identification
Design Identification
system operates in two modes (i) enrollment
mode and (ii) identification.
Enrollment
Biometric
Feature Extractor
Reader
Identification
Feature Extractor
Biometric
Reader
Feature Matcher
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Automatic Identification
Enrollment mode - biometric measurement is
captured - information from raw data extracted
- (feature, person) information is stored
- ID is issued (for verification).
Identification mode - biometric is sensed
(live-scan) - features are extracted from the
raw data - match is performed (search of the
database). In verification mode, person
presents ID. Then system performs match only
against one template in the database.
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Recognition System
Architecture of a typical pattern recognition
system (see A. K. Jain, et al., p. 22).
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Design Issues
Given the speed, accuracy, and cost
specifications 1. How to collect the input
data? (3D, 2D, multiple views, high or low
resolution) 2. Internal representation
(features) for automatic feature extraction 3.
How to extract features? (Algorithms, etc.) 4.
How to select the "matching" metric?
(Measurements are made in specific space) 5. How
to implement it? 6. Organization of database
7. Effective methods for searching a template
in the database (binning, etc.)
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Acquisition
Quality of collected data determines performance
of the entire system. Associated tasks (i)
quality assessment (ii) segmentation
(separation of the data into foreground and
background). Research efforts (i) richer
data (3D, color, etc.) (ii) metrics for
assessment quality of measurements. (iii)
realistic models Solutions enhancement
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Representation

• Which machine-readable representation captures
  the invariant and
• discriminatory information in the data?
• Determine features s.t.
• - invariant for the same individual (intraclass
  variation)
• - maximally distinct for different individuals
  (interclass)
• More distincive features offer more reliable
  identification.
• Representation has to be storage space
  efficient (smart card 2 Kbytes)
• Representation depends on biometrics

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Feature extraction

• Given raw data, automatically extracting the
  given representation is difficult problem.
• Example
• manual fingerprint system uses about a dozen of
  features. For
• automatic system, many of them are not easy to
  reliably detect.
• Feature extraction procedures are typically
  designed in ad hoc manner (inefficient when
  measurements are noisy).
• Determining effective models for features will
  help to reliably
• extract them (esp. in noisy situations).

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Matching
Similarity metric should be robust against -
noise, - structural and statistical variations,
- aging, and artifacts of feature extraction
module. Example signature (hard to define the
ground truth) Performance is determined by (i)
representation and (ii) similarity metric.
Trade-off better engineering design vs. more
complex matcher. Example fingerprint
(variations in features and rigid matcher vs.
Flexible matcher)
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Matching (Fingerprint)
Sources of distortion and noise (i)
inconsistent contact (3D-to-2D) (ii)
non-uniform contact (due to dryness of skin,
sweat, dirt, humidity in the air, etc.)
(iii) irreproducible contact (injuries to the
finger) (iv) feature extraction artifacts
(measurement error) (v) sensing itself adds
noise
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Evaluation
An end-user questions (i) Does the system
makes an accurate identification? (ii)
Is the system sufficiently fast? (iii) What is
the cost of the system? Because of noise,
distortions, and limited information no metric is
adequate for reliable identification.
Decisions - genuine individual, -
imposter.
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Evaluation
Four types of outcomes (1) Genuine individual
is accepted (true) (2) Genuine individual is
rejected (error) (3) Imposter is rejected
(true) (4) Imposter is accepted (error) FAR
- false acceptance rate FRR - false rejection
rate EER - equal error rate Given a
database, performance is a RV and only can be
estimated.
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Evaluation
Measure of performance ROC - receiver
operating curve Confidence Intervals
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Useful Links

• http//www.biometrics.org/
• (publications and periodicals
• research and databases meetings and events)
• http//www.itl.nist.gov/div895/biometrics/
• http//biometrics.cse.msu.edu/
• http//www.tech.purdue.edu/it/resources/biometri
  cs/
• http//www.wvu.edu/bknc/
• http//www.citer.wvu.edu/