An Overview of Biometrics

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An Overview of Biometrics
Luciano Rila
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Contents biometric systems

1. Introduction
2. Biometric identifiers
3. Classification of biometrics methods
4. Biometric system architecture
5. Performance evaluation

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Contentsbiometric technologies

1. Signature recognition
2. Voice recognition
3. Retinal scan
4. Iris scan
5. Face-scan and facial thermogram
6. 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
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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 claimants 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

1. Original
2. Orientation
3. Binarised
4. Thinned
5. Minutiae
6. 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.
• Speakers 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

• Iris
• coloured portion of the eye surrounding the
  pupil.
• complex iris pattern used for identification.

• Retina
• back inside of the eye ball.
• pattern of blood vessels 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.o
  rg/

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Any comments or questions?
luciano.rila_at_rhul.ac.uk