Biometrics
Tasanawan Soonklang
Biometrics
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Biometrics – what is?
Applications – who use?
Operation – how does it work?
Types – what are the different?
Issues – how to choose? , accuracy, concerns
IT related to biometrics
• Movies – some fun
• References – some more readings & links
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Biometrics
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• A term derived from ancient Greek
What is ?
bio = life
metric = to measure
• “Measurement of physiological and
behavioral characteristics to
automatically identify people.”
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Definition
• “The automated approach to
authenticate the identity of a person
using the individual’s unique
physiological or behavioral
characteristics.”
– Yau Wei Yun (2003)
• “Biometrics deals with identification of
individuals based on their biological
or behavioral characteristics”
– Jain et al (1999)
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• Physical/biological characteristics
Characteristics
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Face
Fingerprint
DNA
Hand and finger geometry
Eye structure
Iris
Retina
Ear
Vascular patterns
Odor
Voiceprint
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• Behavioral characteristics
Characteristics
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Signature
Gait
Handwriting
Keystroke
Voice pattern
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• Identification – associating an identity
with an individual
Identification
• Verification (authentication)
– The problem of confirming or denying a
person’s claimed identity (1: 1)
– Am I who I claim I am?
• Recognition (identification)
– The problem of establishing a subject’s
identity (1: Many)
– Who am I?
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• Traditional
Identification Methods
– Something you know: PIN, password...
– Something you have: key, token, card...
But does not insure that you are here and
the real owner.
• Biometrics
– Something you are: a biometric.
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Applications
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• Accurate identification of a person
could deter
Why use ?
– crime and fraud
– streamline business processes
– save critical resources
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• Government
• Military
Who uses ?
• Schools
• Commerce
• Law Enforcement
• Others ?
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Where are it used ?
• Many products such as PC are already using
fingerprints.
• Another big class, historically the first, is the
identification for police application.
• Now, some countries are using biometrics for
immigration control in airport/border patrol.
• Banks are now proposing some ATMs.
• Payment using biometrics is more and more
used in stores.
• Identification of the student in schools.
• Identification of the mother/newborn in
hospitals.
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Operation
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Enrollment
How does it work ?
Capture
Process
Store
No Match
Compare
?
Match
Capture
Process
Verification
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Example
Original source : Anil Jain and Arun Ross (1999)
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Types
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Examples
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Fingerprinting
Palm print
Iris scan
Retinal scan
Facial recognition
Voice recognition
Handwriting recognition
DNA
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• Strength
Fingerprint
– Proven Technology Capable of High
Level of Accuracy
– Range of Deployment Environments
– Ergonomic, Easy-to-Use Device
– Ability to Enroll Multiple Fingers
• Weakness
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Inability to Enroll Some Users
Performance Deterioration over Time
Association with Forensic Application
Need to Deploy Specialized Devices
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• Strength
Palm print
– Ability to Operate in Challenging
Environment
– Established, Reliable Core Technology
– General Perception as Non-intrusive
– Relatively Stable Physiological
Characteristic as Basis
– Combination of Convenience and
Deterrence
• Weakness
– Inherently Limited Accuracy
– Form Factor That Limits Scope of Potential
Applications
– Price
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• Strength
– Resistance to False Matching
– Stability of Characteristic over Lifetime
– Suitability for Logical and Physical Access
• Weakness
Iris
– Difficulty of Usage
– False Non-matching and Failure-to-Enroll
– User Discomfort with Eye-Based
Technology
– Need for a Proprietary Acquisition Device
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• Strength
– it is not easy to change or replicate the
retinal vasculature.
– Supposed to be the most secure
biometric
Retina
• Weakness
– The image acquisition involves
cooperation of the subject
– entails contact with the eyepiece
– requires a conscious effort on the part of
the user.
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• Strength
Face
– Ability to Leverage Existing Equipment
and Image Processing
– Ability to Operate without Physical
Contact or User Complicity
– Ability to Enroll Static Images
• Weakness
– Acquisition Environment Effect on
Matching Accuracy
– Changes in Physiological Characteristics
That Reduce Matching Accuracy
– Potential for Privacy Abuse Due to Noncooperative Enrollment and Identification
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• Strength
Voice
– Ability to Leverage Existing Telephony
Infrastructure
– Synergy with Speech Recognition and
Verbal Account Authentication
– Resistance to Imposters
– Lack of Negative Perceptions Associated
with Other Biometrics
• Weakness
– Effect of Acquisition Devices and
Ambient Noise on Accuracy
– Perception of Low Accuracy
– Lack of Suitability for Today’s PC Usage
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• Strength
Signature
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Resistant to Imposters
Leverages Existing Processes
Perceived as Non-invasive
Users Can Change Signatures
• Weakness
– Inconsistent Signatures Lead to Increased
Error Rates
– Users Unaccustomed to Singing on
Tablets
– Limited Applications
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DNA
• DNA (DeoxyriboNucleic Acid) is the
1D ultimate unique code for one’s
individuality.
• Identification for forensic applications
only.
• Three factors limit the utility of this
biometric for other applications
– Contamination and sensitivity
– Automatic real-time identification issues
– Privacy issues
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Issues
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• Universality – each person should have the
characteristic.
• Uniqueness – is how well the biometric separates
individuals from another.
Comparison
• Permanence – measures how well a biometric
resists aging.
• Collectability – ease of acquisition for
measurement.
• Performance – accuracy, speed, and robustness
of technology used.
• Acceptability – degree of approval of a
technology.
• Circumvention – ease of use of a substitute.
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Comparison
Original source : Yau Wei Yun (2003)
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• How to choose
How to choose ?
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Size of user group
Place of use and the nature of use
Ease of use and user training required
Error incidence such as due to age,
environment and health condition
Security and accuracy requirement
needed
User acceptance level, privacy and
anonymity
Long term stability including technology
maturity, standard, interoperability and
technical support
Cost
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• Failure to Enroll Rate (FTE)
– % of data input is considered invalid and fails to
input into the system.
Accuracy
• False Acceptance Rate (FAR)
– % of invalid users who are incorrectly accepted
as genuine users.
• False Rejection Rate (FRR)
– % of valid users who are rejected as imposters.
• Equal Error Rate (EER)
– The rate at which both accept and reject error
are equal
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FTE
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• scores – to express the similarity between
a pattern and a biometric template.
Scores & Threshold
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FAR & FRR
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The more lower EER, the more accuracy
Relation
Original source : http://www.bioid.com/sdk/docs/About_EER.htm
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• Identify theft and privacy
Concerns
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Using two-factor solution
Biometrics are purely based on matching
Using encryption for matching template
Scanned live biometric data maybe stolen
• Sociological concerns
– Physical harm to an individual
– Personal information through biometric
methods can be misused or sold
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Related to
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Example
• Database
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Storing matching templates
Querying templates
Database management
Security issues
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Example
• Image processing
– Assessing the quality
– Enhancing the image
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• Image processing
Example
a)
b)
c)
d)
e)
The original
A close-up of the original
After 1st stage of thinning
After 2nd stage of thinning
After applying algorithm,
showing bifurcations
(black) and endpoints
(grey)
Original source : http://www.ee.ryerson.ca/opr/research_projects/graph_fingerprint.html
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Example
• Intelligent system
– Pattern classification & recognition
– Decision rules
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• Pattern classification & recognition
– Training and testing data
– Machine learning
Example
Original source : Anil Jain and Arun Ross (1999)
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Recognition
Example
• Information retrieval
– Retrieval templates for recognition
– Scoring
– Evaluation
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Movies
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• Hollywood is using biometrics for
years.
• some truth inside, but sometimes, it is
wrong…
Some fun
• Must see
– Gattaca (1997)
• It was wrong
– The Island (2005)
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• Others
Some fun
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James bond
The Bourne
Minority report
etc. (see the first website in reference)
• Use of some and public concerns
• Physical biometric for identification or
authentication person is the most
widely seen.
• Behavioral biometric much less
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References
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Publications
More readings & links
• Yun, Yau Wei. (2003) The ‘123’ of Biometric Technology. Retrieved from www.
• Jain, Anil, Bolle, Ruud, and Pankanti, Sharath. (1999)
Introduction to biometrics. In: Biometrics, Personal
Identification in Networked Society, pp. 1-41, Springer.
• Jain, Anil, and Ross, Arun. (1999) Introduction to biometrics.
In: Handbook of Biometrics, pp
Lecture notes
• Ioannis Pavlidis. (2003) Introduction to biometrics. In course
cosc6397. Department of Computer Science, University of
Houston.
• Rawitat Pulum. (2006) Introduction to Biometrics. In course
510670. Faculty of Science, Silpakorn University.
Website
• http://pagespersoorange.fr/fingerchip/biometrics/biometrics.htm
• http://en.wikipedia.org/wiki/Biometrics
• http://www.bioid.com/sdk/docs/About_EER.htm
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Relation
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The more lower EER, the more accuracy
Relation
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