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BiometricsTasanawan Soonklang
2
Bio
metrics
• Biometrics – what is?
• Applications – who use?
• Operation – how does it work?
• Types – what are the different?
• Issues – how to choose? , accuracy, concerns
• IT r elated to biometrics
• Movies – some fun
• R eferences – s ome more readings & links
3
Biometrics
4
What is ?
• A term derived from ancient Greek
bio = lifemetric = to measure
• “Measurement of physiological and behavioral characteristics to automatically identify people.”
5
Definitio
n
• “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)
6
Ch
ara
cteristics
• Physical/biological characteristics– Face – Fingerprint – DNA – Hand and finger geometry – Eye structure – Iris – Retina – Ear – Vascular patterns – Odor– Voiceprint
7
Ch
ara
cteristics
• Behavioral characteristics– Signature – Gait – Handwriting – Keystroke – Voice pattern
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Iden
tifica
tion
• Identification – associating an identity with an individual
• 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?
9
Iden
tifica
tion M
eth
od
s
• Traditional– 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.
10
Applications
11
Why u
se ?
• Accurate identification of a person could deter – crime and fraud– streamline business processes – save critical resources
12
Who u
ses ?
• Government
• Military
• Schools
• Commerce
• Law Enforcement
• Others ?
13
Where
are
it use
d ?
• 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.
14
Operation
15
Enrollment
CaptureCapture ProcessProcess StoreStore
How
does it w
ork ?
Verification
ProcessProcess
No MatchNo Match
MatchMatch
CaptureCapture
Compare
?
16
Exam
ple
Original source : Anil Jain and Arun Ross (1999)
17
Types
18
Exam
ple
s
• Fingerprinting • Palm print• Iris scan • Retinal scan • Facial recognition • Voice recognition • Handwriting recognition • DNA
19
Fingerp
rint
• Strength– Proven Technology Capable of High
Level of Accuracy– Range of Deployment Environments– Ergonomic, Easy-to-Use Device– Ability to Enroll Multiple Fingers
• Weakness– Inability to Enroll Some Users– Performance Deterioration over Time– Association with Forensic Application– Need to Deploy Specialized Devices
20
Palm
prin
t
• Strength– 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
21
Iris
• Strength– Resistance to False Matching– Stability of Characteristic over Lifetime– Suitability for Logical and Physical
Access
• Weakness– Difficulty of Usage– False Non-matching and Failure-to-
Enroll– User Discomfort with Eye-Based
Technology– Need for a Proprietary Acquisition
Device
22
Retin
a
• Strength– it is not easy to change or replicate the
retinal vasculature.– Supposed to be the most secure
biometric
• Weakness– The image acquisition involves
cooperation of the subject– entails contact with the eyepiece– requires a conscious effort on the part
of the user.
23
Face
• Strength– 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 Non-
cooperative Enrollment and Identification
24
Voice
• Strength– 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
25
Sig
natu
re
• Strength– 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
26
DN
A
• 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
27
Issues
28
Com
pariso
n
• Universality – each person should have the characteristic.
• Uniqueness – is how well the biometric separates individuals from another .
• Permanence – measures how well a biometric r esists aging.
• Collectability – ease of acquisition for measurement .
• Performance – accuracy, speed, and robustnes s of technology used.
• Acceptability – degree of approval of a technology .
• Circumvention – ease of use of a substitute.
29
Com
pariso
n
Original source : Yau Wei Yun (2003)
30
How
to ch
oose
?
• How to choose– 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
31
Accu
racy
• Failure to Enroll Rate (FTE)– % of data input is considered invalid and fails to i
nput into the system.
• 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
32
FTE
33
Sco
res &
Th
resh
old
• scores – to express the similarity between a pattern and a biometric template .
34
FAR
& FR
R
35
Rela
tion
The more lower EER, the more accuracy
Original source : http://www.bioid.com/sdk/docs/About_EER.htm
36
Con
cern
s
• Identify theft and privacy– 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
37
Related to
38
Exam
ple
• Database– Storing matching templates– Querying templates– Database management– Security issues
39
Exam
ple
• Image processing– Assessing the quality– Enhancing the image
40
Exam
ple
• Image processing
a) The originalb) A close-up of the originalc) After 1st stage of thinningd) After 2nd stage of thinninge) After applying algorithm,
showing bifurcations (black) and endpoints (grey)
Original source : http://www.ee.ryerson.ca/opr/research_projects/graph_fingerprint.html
41
Exam
ple • Intelligent system
– Pattern classification & recognition– Decision rules
42
Exam
ple
• Pattern classification & recognition– Training and testing data– Machine learning
Original source : Anil Jain and Arun Ross (1999)
43
Exam
ple • Information retrieval
– Retrieval templates for recognition– Scoring– Evaluation
Recognition
44
Movies
45
Som
e fu
n
• Hollywood is using biometrics for years.
• some truth inside, but sometimes , it i s wrong…
• Must see– Gattaca (1997)
• It was wrong– The Island (2005)
46
Som
e fu
n
• Others– 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
47
References
48
More
readin
gs &
links
Publications• 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://pagesperso-orange.fr/fingerchip/biometrics/
biometrics.htm• http://en.wikipedia.org/wiki/Biometrics• http://www.bioid.com/sdk/docs/About_EER.htm
49
Rela
tion
50
Rela
tion
The more lower EER, the more accuracy