an overview of biometrics
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An Overview of Biometrics. Luciano Rila. Contents – biometric systems. Introduction Biometric identifiers Classification of biometrics methods Biometric system architecture Performance evaluation. Contents biometric technologies. Signature recognition Voice recognition Retinal scan - PowerPoint PPT PresentationTRANSCRIPT
Luciano Rila/RHUL 1
An Overview of Biometrics
Luciano Rila
Luciano Rila/RHUL 2
Contents – biometric systems
1. Introduction2. Biometric identifiers3. Classification of biometrics methods4. Biometric system architecture5. Performance evaluation
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Contentsbiometric technologies
6. Signature recognition7. Voice recognition8. Retinal scan9. Iris scan10. Face-scan and facial thermogram11. Hand geometry
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Personal identification
Association of an individual with an identity:
Verification (or authentication): confirms or denies a claimed identity.
Identification (or recognition): establishes the identity of a subject (usually from a set of enrolled persons).
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Personal identification objects
Token-based: “something that you have”
Knowledge-based: “something that you know”
Biometrics-based: “something that you are”
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Biometrics
Bio + metrics:The statistical measurement of biological data.--Biometric Consortium definition:Automatically recognising a person using
distinguishing traits.
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Some applications
Financial security (e-fund transfers, ATM, e-commerce, e-purse, credit cards),
Physical access control, Benefits distribution, Customs and immigration, National ID systems, Voter and driver registration, Telecommunications (mobile, TV)
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Biometric identifiers
Universality Uniqueness Stability Collectability
Performance Acceptability Forge resistance
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Biometric technologies
Covered in ISO/IEC 27N2949:– recognition of signatures,– fingerprint analysis,– speaker recognition,– retinal scan,– iris scan,– face recognition,– hand geometry.
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Other biometric methods
Found in the literature:– vein recognition (hand),– keystroke dynamics,– palmprint,– gait recognition,– body odour measurements,– ear shape.
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Classification of biometrics methods
Static:– fingerprint– retinal scan– iris scan– hand geometry
Dynamic:– signature recognition– speaker recognition
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Biometric system architecture
Basic modules of a biometric system:– Data acquisition– Feature extraction– Matching– Decision– Storage
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Biometric system model
Raw data Extracted features
template
Authentication decision
Data collection Signal
processing
matching storage
score
decision Application
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Data acquisition module
Reads the biometric info from the user. Examples: video camera, fingerprint
scanner/sensor, microphone, etc. All sensors in a given system must be similar to
ensure recognition at any location. Environmental conditions may affect their
performance.
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Feature extraction module
Discriminating features extracted from the raw biometric data.
Raw data transformed into small set of bytes – storage and matching.
Various ways of extracting the features. Pre-processing of raw data usually
necessary.
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Matching module
The core of the biometric system. Measures the similarity of the claimant’s
sample with a reference template. Typical methods: distance metrics,
probabilistic measures, neural networks, etc. The result: a number known as match score.
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Decision module
Interprets the match score from the matching module.
Typically a binary decision: yes or no. May require more than one submitted
samples to reach a decision: 1 out of 3. May reject a legitimate claimant or accept
an impostor.
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Storage module
Maintains the templates for enrolled users. One or more templates for each user. The templates may be stored in:
– a special component in the biometric device,– conventional computer database,– portable memories such as smartcards.
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Enrolment
Capturing, processing and storing of the biometric template.
Crucial for the system performance. Requirements for enrolment:
– secure enrolment procedure,– check of template quality and “matchability”,– binding of the biometric template to the
enrollee.
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Possible decision outcomes
A genuine individual is accepted. A genuine individual is rejected (error). An impostor is rejected. An impostor is accepted (error).
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Errors
Balance needed between 2 types of error:– Type I: system fails to recognise valid user
(‘false non-match’ or ‘false rejection’).– Type II: system accepts impostor (‘false match’
or ‘false acceptance’). Application dependent trade-off between
two error types.
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Pass rates
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Tolerance threshold Error tolerance threshold is crucial and
application dependent. Tolerance too large gives Type II error
(admit impostors). Tolerance too small gives Type I errors
(reject legitimate users). Equal error rate for comparison: false non-
match equal to false match.
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Biometric technologies
Signature recognition Voice recognition Retinal scan Iris scan Face biometrics Hand geometry
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Signature recognition
Signatures in wide use for many years. Signature generating process a trained
reflex - imitation difficult especially ‘in real time’.
Automatic signature recognition measures the dynamics of the signing process.
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Dynamic signature recognition
Variety of characteristics can be used:– angle of the pen,– pressure of the pen,– total signing time,– velocity and acceleration,– geometry.
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Signature recognition: advantages disadvantages
Advantages:– Resistance to forgery– Widely accepted– Non-intrusive– No record of the
signature
Disadvantages:– Signature
inconsistencies– Difficult to use– Large templates
(1K to 3K)
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Fingerprint recognition
Ridge patterns on fingers uniquely identify people.
Classification scheme devised in 1890s. Major features: arch, loop, whorl. Each fingerprint has at least one of the
major features and many ‘small’ features.
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Features of fingerprints
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Fingerprint recognition (cont.)
In a machine system, reader must minimise image rotation.
Look for minutiae and compare. Minor injuries a problem. Automatic systems can not be defrauded by
detached real fingers.
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Fingerprint authentication
Basic steps for fingerprint authentication:– Image acquisition,– Noise reduction,– Image enhancement,– Feature extraction,– Matching.
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Fingerprint processing
a) Original
b) Orientation
c) Binarised
d) Thinned
e) Minutiae
f) Minutia graph
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Fingerprint recognition: advantages disadvantages
Advantages:– Mature technology– Easy to use/non-
intrusive– High accuracy– Long-term stability– Ability to enrol
multiple fingers
Disadvantages:– Inability to enrol
some users– Affected by skin
condition– Association with
forensic applications
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Speaker recognition
Linguistic and speaker dependent acoustic patterns.
Speaker’s patterns reflect:– anatomy (size and shape of mouth and throat),– behavioral (voice pitch, speaking style).
Heavy signal processing involved (spectral analysis, periodicity, etc)
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Speaker recognition systems
Text-dependent: predetermined set of phrases for enrolment and identification.
Text-prompted: fixed set of words, but user prompted to avoid recorded attacks.
Text-independent: free speech, more difficult to accomplish.
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Speaker recognition: advantages disadvantages
Advantages:– Use of existing
telephony infrastruct– Easy to use/non-
intrusive/hands free– No negative
association
Disadvantages:– Pre-recorded attack– Variability of the
voice– Affected by noise– Large template
(5K to 10K)
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Eye biometric
Retina:– back inside of the eye ball.
– pattern of blood vessels used for identification.
Iris:– coloured portion of the eye surrounding the pupil.
– complex iris pattern used for identification.
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Retinal pattern
Accurate biometric measure. Genetically independent: identical twins
have different retinal pattern. Highly protected, internal organ of the eye. May change during the life of a person.
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Retinal scan: advantages disadvantages
Advantages:– High accuracy– Long-term stability– Fast verification
Disadvantages:– Difficult to use– Intrusive– Limited applications
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Iris properties
Iris pattern possesses a high degree of randomness: extremely accurate biometric.
Genetically independent: identical twins have different iris pattern.
Stable throughout life. Highly protected, internal organ of the eye. Patterns can be acquired from a distance (1m). Patterns can be encoded into 256 bytes.
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Iris recognition
Iris code developed by John Daugman at Cambridge.
Extremely low error rates. Fast processing. Monitoring of pupils oscillation to prevent fraud. Monitoring of reflections from the moist cornea
of the living eye.
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The iris code
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Iris recognition: advantages disadvantages
Advantages:– High accuracy– Long term stability– Nearly non-intrusive– Fast processing
Disadvantages:– Not exactly easy to
use– High false non-
match rates– High cost
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Face-scan and facial thermograms
Static controlled or dynamic uncontrolled shots.
Visible spectrum or infrared (thermograms). Non-invasive, hands-free, and widely
accepted. Questionable discriminatory capability.
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Face recognition
Visible spectrum: inexpensive. Most popular approaches:
– eigenfaces,– Local feature analysis.
Affected by pose, expression, hairstyle, make-up, lighting, eyeglasses.
Not a reliable biometric measure.
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Face recognition: advantages disadvantages
Advantages:– Non-intrusive– Low cost– Ability to operate
covertly
Disadvantages:– Affected by
appearance/environment– High false non-match
rates– Identical twins attack– Potential for privacy
abuse
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Facial thermogram
Captures the heat emission patterns derived from the blood vessels under the skin.
Infrared camera: unaffected by external changes (even plastic surgery!) or lighting.
Unique but accuracy questionable. Affected by emotional and health state.
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Facial thermogram: advantages disadvantages
Advantages:– Non-intrusive– Stable– Not affected by
external changes– Identical twins
resistant– Ability to operate
covertly
Disadvantages:– High cost (infrared
camera)– New technology– Potential for privacy
abuse
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Hand geometry
Features: dimensions and shape of the hand, fingers, and knuckles as well as their relative locations.
Two images taken: one from the top and one from the side.
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Hand geometry: advantages disadvantages
Advantages:– Not affected by
environment– Mature technology– Non-intrusive– Relatively stable
Disadvantages:– Low accuracy– High cost– Relatively large readers– Difficult to use for
some users (arthritis, missing fingers or large hands)
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Choosing the biometrics
Does the application need identification or authentication?
Is the collection point attended or unattended?
Are the users used to the biometrics? Is the application covert or overt?
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Choosing the biometrics (cont.)
Are the subjects cooperative or non-cooperative?
What are the storage requirement constraints?
How strict are the performance requirements?
What types of biometrics are acceptable to the users?
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References ISO/DIS 21352: Biometric information management and
security, ISO/IEC JTC 1/SC 27 N2949. Scheuermann, Schwiderski-Grosche, and Struif, “Usability
of Biometrics in Relation to Electronic Signatures”, GMD Report 118, Nov. 2000.
Jain et al., “Biometrics: Personal Identification in Networked Society,” Kluwer Academic Publishers.
Nanavati et al., “Biometrics: Identity Verification in a Networked Society,” Wiley.
The Biometric Consortium: http://www.biometrics.org/