Transcript Fingerprint Authentication Dr. Lynne Coventry

Fingerprint
Authentication
Dr. Lynne Coventry
What is Biometrics?
• Biometrics can be defined as the use of
anatomical, physiological or behavioural
characteristics to recognise an individual or
verify the claimed identity of an individual.
• Techniques use characteristics of
– Fingerprint
 Eyes
– Face
 Hands
– Voice
 Signature
– Walk
 Typing
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Why use Biometrics?
• Biometrics techniques are used to confirm
that a person is actually present, rather
than just their token or identifier.
• A name, password, key, card, PIN, number,
specific knowledge (e.g. mother’s maiden
name) does not confirm the presence of the
legitimate owner – they can only confirm
that the correct token or knowledge is being
used and assume that the user is genuine.
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Considerations for ATM use
• What needs to be considered before
deploying a customer-facing biometrics
solution at an ATM?
– Users
– Environment
– System
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User Considerations
• User Base (Number of users)
• Outliers (People who cannot use system
(FTE)
• Enrolment + Training requirements
• Accessibility issues
• Usability (speed, errors, attitude)
• Public Acceptance
•
•
•
•
Perceived Security
Privacy
Speed
Hygiene
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Why Fingerprint?
• Fingerprint is considered one of the most effective
techniques but there can be problems with dirt, dry
or worn prints and also with very fine prints.
• Fingerprint sensors are small and low cost (typ.
$10 for sensor) and easy to integrate/replace.
• They can be deliberately damaged.
• Template sizes tend to be small (<1k) so easy to
move and store.
• Can match 1-to-1 in few (typically 2) seconds.
• Public awareness and exposure.
• Requires positive user participation. Contact is
necessary. Finger placement is important.
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Why Radio Frequency?
• The system tested has a unique, patented image
capture device based on active RF signal detection
• It has security, dirt resistance, spoof resistance
built in to the chip
• Comparing the main variable criteria below to the
other main fingerprint image capture techniques it
is clearly the best system
Technique
Size
Cost
Ease of Use
Dirt Affected
Wear Affected
Easily Duped
Optical
Small
Low
Easy
Yes
Yes
Easy
Capacitance
Small
V. Low
Easy
Yes
Yes
Easy
RF
Small
V. Low
Easy
No
No
Difficult
Ultrasound
V. Large
V. High
Easy
No
Yes
Medium
Thermal
V.Small
Low
Difficult
Yes
Yes
Medium
Pressure
Small
V. Low
Easy
Yes
Yes
Medium
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Dynamic Optimization:
Dry Finger
• This example took
4 frames
• Executed in about
½ second on a PC
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Adjust A/D
references
3
Increase amplifier
gain
2
Increase drive
signal
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In Slow Motion
...
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Testing Biometrics
• Method affects performance achieved
• Lab conditions with small homogenous set
of good trained young cooperative users
• Real world has great variabilty, uninformed
and even hostile users
• Performance Measures
– FAR
– FRR
– FTE
– FTA
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Fingerprint Proof of Principle
• Maximise usability and acceptance
of fingerprint at the ATM
• Running usability trials and
iteratively designing interaction
– inhouse intuitive behaviour, sensor size
– inhouse iterative design and evaluation
of leadthrough
– full consumer trial
• Rewriting application for self service
environment
• Investigating integration issues with
ATM software
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Study 1: Size and intuition
• 76 users (enrol + 10 V)
• Enrolment results.
• Only 2 people failed to enrol
• 14 people were asked by the system to
repeat the enrolment
• 10% validation errors (FR)
– High individual variability.
– Quality and core placement issues
– Small sensor is acceptable but
requires software refinement.
• Use core information to help the user
– Good enrolment is paramount to
successful validation.
– Need for education about
fingerprint core
• What was required as well as how
to do it
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Study 2: Improving training
and leadthrough
• Iterative development
– moving red line
– Taught to locate core
– Teaching standardised
– software leadthrough to use the
core to tell the user how to
move
• Results
– removed all failure to enrol
– more consistent performance
– Only 2.5% false rejects
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Study 3: Consumer Trial
• Use representative general public
group across age, gender and
occupation
• 168 Participants
– Random convenience sample, recruited in
Edinburgh
– 60% under 50 and 40% over 50
– 52% male 48% female
• Identify attitude/acceptance
• Identify remaining usability
issues:
– Improved enrolment
– Improved leadthrough (target versus
image)
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Selected findings
• Still no real usage of biometrics
– 97% never used biometrics before
• Insecure behaviour
– 24% have their PIN written down
– 26% share their PIN and card
• Now percieved need
– Security and Convenience both an advantage and a
disadvantage for fingerprint and PIN
• New technology concerns remain (50%)
• Privacy concerns remain for minority (20%)
• General willingness to accept (60 -> 70%)
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Comparison between Averages of PIN and Fingerprint Agreement Statements
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Average Agreement (+2)/Disagreement (-2)
1.5
1
0.5
0
Secure *
Easy *
Acceptable *
Fast
Reliable
Hygienic *
Stressful *
Trust Bank
PIN
Fingerprint
* Significant at 5%
-0.5
-1
-1.5
-2
Statements
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Performance
• 13% failure to enrol rate
– Problems getting good enrolment images
– image quality
– all from over 60’s mainly female
• 10% false reject rate
– poor templates
– inconsistent placement of finger
– placing more restriction on placement or image
quality will increase failure to acquire
• Still to complete more detailed analysis of
performance
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Usability issues remain
• People do not understand the concept of the
fingerprint core
• A central core image is essential
• People tend to place their finger too low on
the sensor
• Pre-training is crucial to successful
enrolment
• Good enrolments form the basis of
consistent validation
• Still need human intervention in the
enrolment process
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Future Trials
• Explanation of technology for participants
• Explain difference between verification and
identification
• Trial new RF device
• same size, higher DPI
• Trial concerning problem group
– Elderly above 60
• Improvement in leadthrough
– combine target and image leadthrough
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Biometrics evaluations conclusions
Actual
uptake
Use
Pluralistic
approach
Pilot
Lab tests
Functional
Prototype tests
Focus Groups
Real acceptance
with customers
Fingerprint,
iris, PIN
Potential acceptance
with representative user
population
Speech, facial,
fingerprint, iris,
PIN
Speech, facial,
fingerprint, finger swipe,
iris, PIN
usability
Worries,
problems,
fears
Speech, hand geometry, finger geometry,
facial, fingerprint, iris, PIN
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Conclusions
• Biometrics can increase security
and improve risk management
• For niche applications, biometrics
makes good business sense, are
popular and appear to be
successful
• Successful biometrics systems
are dependent on successful
enrolment
• For the general ATM user
population usability issues will
impact security.
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