An Overview of Biometrics

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.


Classification of biometrics methods

Static:– fingerprint– retinal scan– iris scan– hand geometry

Dynamic:– signature recognition– speaker recognition


Biometric system architecture

Basic modules of a biometric system:– Data acquisition– Feature extraction– Matching– Decision– Storage


Biometric system model

Raw data Extracted features

template

Authentication decision

Data collection Signal

processing

matching storage

score

decision Application


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.


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.


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.


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.


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.


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.


Possible decision outcomes

A genuine individual is accepted. A genuine individual is rejected (error). An impostor is rejected. An impostor is accepted (error).


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.


Pass rates


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.


Biometric technologies

Signature recognition Voice recognition Retinal scan Iris scan Face biometrics Hand geometry


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.


Dynamic signature recognition

Variety of characteristics can be used:– angle of the pen,– pressure of the pen,– total signing time,– velocity and acceleration,– geometry.


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)


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.


Features of fingerprints


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.


Fingerprint authentication

Basic steps for fingerprint authentication:– Image acquisition,– Noise reduction,– Image enhancement,– Feature extraction,– Matching.


Fingerprint processing

a) Original

b) Orientation

c) Binarised

d) Thinned

e) Minutiae

f) Minutia graph


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


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)


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.


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)


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.


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.


Retinal scan: advantages disadvantages

Advantages:– High accuracy– Long-term stability– Fast verification

Disadvantages:– Difficult to use– Intrusive– Limited applications


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.


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.


The iris code


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


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.


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.


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


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.


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


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.


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)


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?


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?


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/

http://www.biometrics.org/


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An Overview of Biometrics