1. Biometrics Angela Sasse – Dept of Computer Science

2. Goals of this lecture What are biometrics? How they are applied
Usability and security issues

3. See http://www.bromba.com for a good FAQ on Biometric jargon
biometric = biological or behavioural property of an individual that can be measured and from which distinguishing, repeatable biometric features can be extracted for the purpose of automated recognition of individuals
biometric sample = analog or digital representation of biometric characteristics prior to biometric feature extraction process and obtained from a biometric capture device or biometric capture subsystem (raw data)
biometric template = stored biometric features, applied to the biometric features of a recognition biometric sample during a comparison to give a comparison result.
See for a good FAQ on
Biometric jargon

4. Some basics Verification using ID + biometric, or
Enrolment = capture of biometric feature and generation of biometric sample and/or template
Full images or templates
templates are more efficient
Images can be used to reverse-id/create new templates
Verification using ID + biometric, or
identification (biometric compared to database

5. Physical/behavioural
Fingerprint
Finger / Palm Vein
Hand geometry
Face recognition
Iris
Retina
Earshape
Behavioural
Voice print
Dynamic Signature Recognition (DSR)
Typing pattern
Gait recognition
Heart rate analysis

6. Enrolment Crucial for security and subsequent performance
In some context, identity of enrolee needs to be checked
Biometrics enrolled need to be
genuine (see attacks)
good enough quality to work
Enrolment procedure needs to be formalised
Staff need to be trained
Staff need to be trustworthy or closely checked
Time taken to carry out enrolment often under-estimated

7. FTE
FTE (failure to enrol) rate = proportion of people who fail to be enrolled successfully
FTAs: users can be enrolled but biometric sample too poor quality to verify
Reasons for FTE/FTA
Biometric not present or temporarily inaccessible
Biometric not sufficiently prominent or stable
Problem for Universal Access – may exclude
Older users
Disabled
Equipment may be too difficult to use

8. FTE in UKPS enrolment trial
Face
Iris
Finger
Quota
0.15%
12.30%
0.69%
Disabled
2.73%
39%
3.91%
UKPS (UK Passport Service) enrolment trial 2004

9. FAR & FRR FAR (False Acceptance Rate)
accepting user who is not registered
mistaking one registered user for another
High security: FAR of .01% acceptable
FRR (False Rejection Rate)
– rejecting legitimate user
High FRRs reduce usability, high FARs reduce security
customer-based applications tend to raise FAR

10. Performance User performance depends on frequency of use:
Frequent users complete faster and with fewer errors, infrequent users need step-by-step guidance and detailed feedback
Degree of cooperation
Total usage time (not just for matching)
Quality of enrolled and presented samples has key impact (e.g. fingerprints 1 or 10 at a time?)
Different performance for identification and verification (1-1 verification or 1-many identification)

13. "We were aiming for it to scan 12 pupils a minute, but it was only managing 5 so has been temporarily suspended as we do not want pupils' meals getting cold while they wait in the queue."
Careful balancing of business process requirements
and security requirements needed

14. Average 12-20 seconds, longer with infrequent users
Total Usage Process
Time quoted by suppliers often only refer to capture of live image & matching
Walk up to machine
Put down bags, remove hats, etc.
Find token (if used)
Put on token (if used)
Read token
Wait for live image to be captured & matched
Walk away & free machine for next user
Plus average number of rejections & re-tries
Average seconds, longer with infrequent users

15. FRR in UKPS enrolment trial
Face
Iris
Finger
Quota
Time:
30.82%
39 sec
1.75%
58 sec
11.70%
1 min 13 sec
Disabled
51.57%
1 min 3 sec
8.22%
1 min 18 sec
16.35%
1 min 20 sec

16. Performance: Smartgate Sydney Airport
Problem: speedy & secure immigration
Technology: Face recognition system
Users: Quantas air crew (2000)
Performance:
FAR “less than 1%”
FRR 2%
“could be faster” (average 12 secs)
Several re-designs necessary, including updating of image templates

17. Example: BKA face recognition trial
Railway station with 20,000 passengers/day
2 month trial of 3 systems
200 people on watch list, who passed through every day, making no effort to conceal their identity
FAR fixed at .1% (= 23 false alarms/day)
Best performing system at under most favourable detected caught 60% (down to 20%)

18. Usability Issues: Finger
Which finger?
How to position
Where on sensor?
Which part of finger?
Straight or sideways?
Problems: arthritis, long fingernails, handcreme, circulation problems

19. Which finger?

20. Finger position?

21. Usability Issues: Iris
What is it – iris or face?
One or both eyes?
One eye: how to focus?
Distance adjustment
Positioning
“rocking” or “swaying”
Glasses and contact lenses
about half of population wear them
Target area difficult to see when glasses are removed
Example: Project IRIS at Heathrow

22. Focussing

23. Height adjustment
Often not sufficient for very short (under 1.55 m) or very tall (over 2.10) people, or wheelchair users
Need to use hand to adjust
If card needs to be held, other things users carry or hold need to be put down

24. Height adjustment

25. … but users may not realise this
… or be reluctant to
touch equipment,
or think it takes too long

26. Usability Issues: Face
What is it?
Where do I stand?
Where do I look/what am I looking at?
Standing straight, keeping still
“Neutral expression”
Hats, changes in (facial) hair, makeup

27. Distance

28. “Neutral expression”

29. User Acceptance Issues –Finger
Hygiene, Hygiene, Hygiene
Association with forensics/criminals
Finger chopped off

31. Liveness detection Detects movement, pulse, blood flow
Fitted to several systems, but tends to increase FRR
Users: fine, but do the criminals know about it?

32. User Acceptance Issues - Iris
Risk to health (e.g. damage to eyes, triggering epilepsy)
Covert medical diagnosis
Illnesses (iridology)
Pregnancy
Drugs
“Minority Report” attacks

33. User Acceptance Issues - Face
Covert identification
Surveillance/tracking
Direct marketing

34. User Acceptance – General Issues
Data protection – threat to  privacy
Abuse by employer, commercial organisations, state, or malicious individuals
Mission creep
Increasing capability of technology – e.g. iris recognition at a distance
Integration with other technologies – e.g. RFID
Doubts about reliability
Sophisticated attackers
Can government really keep systems secure?
Cheap systems and successful attacks erode confidence

36. Attacks - Finger Simple
Activate latent prints: breathing, bag with warm water
Sophisticated
Lift print with tape or photograph
Gelatine print (gummy bear attack) – lasts 1x
Silicone print

37. CCC strikes again Pay-by-touch system in German supermarket chain
Superglue
Plastic bottle cap
Digital camera
PC with laser printer
Plastic foil
Wood glue
Published fingerprint of German Home Secretary

38. Attacks - Iris Simple Picture of eye stuck on glasses Sophisticated
Coloured contact

39. Attacks - Face Simple Replay attack (Photo or video of person)
Glasses with strong frames
Sophisticated
Mask (Mission Impossible attack)