biometrics angela sasse – dept of computer science

Biometrics

Angela Sasse – Dept of Computer Science

GA10 Authentication 3: Biometrics

Goals of this lecture

1. What are biometrics?2. How they are applied3. Usability and security issues


• 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 http://www.bromba.com for a good FAQ on Biometric jargon

http://www.bromba.com/


Some basics

• 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


Physical/behavioural

Physical • 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


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


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


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


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


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)


"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


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 12-20 seconds, longer with infrequent users


FRR in UKPS enrolment trial

Face Iris Finger

QuotaTime:

30.82%39 sec

1.75%58 sec

11.70%1 min 13 sec

DisabledTime:

51.57%1 min 3 sec

8.22%1 min 18 sec

16.35%1 min 20 sec


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


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%)


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


Which finger?


Finger position?


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


Focussing


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


Height adjustment


… but users may not realise this

… or be reluctant to touch equipment, or think it takes too long


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


Distance


“Neutral expression”


User Acceptance Issues –Finger

• Hygiene, Hygiene, Hygiene

• Association with forensics/criminals

• Finger chopped off


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?


User Acceptance Issues - Iris

• Iris– Risk to health

(e.g. damage to eyes, triggering epilepsy)– Covert medical diagnosis

• Illnesses (iridology)• Pregnancy• Drugs

• “Minority Report” attacks


User Acceptance Issues - Face

• Covert identification• Surveillance/tracking

– Direct marketing


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


• 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

Attacks - Finger


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


Attacks - Iris

• Simple– Picture of eye stuck on

glasses• Sophisticated

– Coloured contact


Attacks - Face

• Simple– Replay attack (Photo or

video of person)– Glasses with strong

frames• Sophisticated

– Mask (Mission Impossible attack)

http://www.heise.de/ct/english/02/11/114/bild7.jpg

biometrics angela sasse – dept of computer science

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