Bias in Face Recognition: What does that even mean? … in Face Recognition: What does that even...

BiasinFaceRecognition:Whatdoesthatevenmean?Andisitserious?

PatrickGrotherInformationTechnologyLaboratory

NationalInstituteofStandardsandTechnologyUnitedStatesDepartmentofCommerce

BiometricsCongress,LondonNovember2,2017

QuotingGeorgetown’sReport:“ThePerpetualLine-up”

• Themostprominentstudy[Klareetal.]foundthatseveralleadingalgorithmsperformedworseonAfricanAmericans,women,andyoungadultsthanonCaucasians,men,andolderpeople,respectively.216

• IfthesuspectisAfricanAmericanratherthanCaucasian,thesystemismorelikelytoerroneouslyfailtoidentifytherightperson,potentiallycausinginnocentpeopletobebumpedupthelist—andpossiblyeveninvestigated

• “Q:IstheBookingPhotoComparisonSystembiasedagainstminorities[?]”• “A:No…itdoesnotseerace,sex,orientationorage.Thesoftwareismatchingdistanceandpatternsonly,

notskincolor,ageorsexofanindividual.”- FrequentlyAskedQuestions,SeattlePoliceDepartment

• Thereisnoindependenttestingregimeforraciallybiasederrorrates… twomajorfacerecognitioncompaniesadmittedthattheydidnotrunthesetests

• Racialbiasintrinsictoanalgorithmmaybecompoundedbyoutsidefactors.AfricanAmericansaredisproportionatelylikelytocomeintocontactwith—andbearrestedby—lawenforcement.218

[Bias]

[Priors]

[NoTestsForBias]

ClareGarvie,AlvaroM.Bedoya,JonathanFrankleThePerpetualLine-upUnregulatedPoliceFaceRecognitionInAmerica

GeorgetownLawCenteronPrivacyandTechnologyOctober18,2016https://www.perpetuallineup.org/

[Awareness]

[Conseq-uence]

RelevantQuantities

PPriorProbability

Demographicsinlawenforcementdatabases≠generalpopulation.

InUSA:• Moremale• Moreblack• Younger

FailuretoEnrol(ImageQuality)

+Exposure

-Exposure

FNMR1:1FalseRejection

FMR1:1FalseAccept

Accuracy

⟶ Inconvenience

⟶ Securityhole

FNIR1:N“MissRate”

FPIR1:N“FalseAlarm”

⟶Missedleadininvestigation

⟶ Falselead:wasteoftime

⟶Wastedeffortonothers

⟶ Displacesactuallead

http://www.telegraph.co.uk/technology/2016/12/07/robot-passport-checker-rejects-asian-mans-photo-having-eyes/

+

+

ErrorTradeoffCharacteristic:Interpretationdifficulty

3divi_000 dermalog_001 dermalog_002

neurotechnology_000 ntechlab_000 rankone_000

rankone_001 tongyitrans_001 vigilantsolutions_000

0.01

0.03

0.05

0.10

0.20

0.30

0.01

0.03

0.05

0.10

0.20

0.30

0.01

0.03

0.05

0.10

0.20

0.30

1e−05 1e−04 1e−03 1e−02 1e−01 1e−05 1e−04 1e−03 1e−02 1e−01 1e−05 1e−04 1e−03 1e−02 1e−01False match rate (FMR)

Fals

e no

n−m

atch

rate

(FN

MR

) Sex

F

M

Race

B

W




0.01

0.03

0.05

0.10

0.20

0.30

0.01

0.03

0.05

0.10

0.20

0.30

0.01

0.03

0.05

0.10

0.20

0.30


Fals

e no

n−m

atch

rate

(FN

MR

) Sex

F

M

Race

B

W

FalseNon-match

FalsematchrateSource:FRVT2017using~600Kmugshots. https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt-ongoing

But1:1systemsoperateatfixedT,notfixedFMR.




0.01

0.03

0.05

0.10

0.20

0.30

0.01

0.03

0.05

0.10

0.20

0.30

0.01

0.03

0.05

0.10

0.20

0.30


Fals

e no

n−m

atch

rate

(FN

MR

) Sex

F

M

Race

B

W




0.01

0.03

0.05

0.10

0.20

0.30

0.01

0.03

0.05

0.10

0.20

0.30

0.01

0.03

0.05

0.10

0.20

0.30


Fals

e no

n−m

atch

rate

(FN

MR

) Sex

F

M

Race

B

W

Falsenon-matchrate

Falsematchrate

Conclusions:1:1Accuracyvariesbysex,race

• Womenlessaccuratelyverified,bothFMRandFNMRhigherthanmen

• AfricanAmericansgiveslightlylowerFNMRthanWhites

• AfricanAmericansgivemuchhigherFMRthanWhites

Source:FRVT2017using~600Kmugshots. https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt-ongoing

FaceRecognitionAccuracy:EffectofAge(notAging)

Falserejectioninanoperationalsystem:ByUserAge

1. Thetableshowsfalsenon-matchratesforpassport-to-liveauthentication.

2. ThetimeelapsedbetweenpassportissuanceandABDtransactionisignoredinthisanalysis

3. Themostpopulousagegroupis40-somethings.

TAKEAWAYS:• Atthecurrentoperatingthreshold,false

rejectionsdeclinesteadilywithageoftheuser

• Youngadultsfailtoverifytwiceasoftenas50-somethings.

# Ageat timeofABDtransaction

FNMR Numberoftransactions

1 (0, 6] 0.13 38

2 (6,12] 0.11 386

3 (12,18] 0.07 1430

4 (18,24] 0.06 1036

5 (24,30] 0.05 1055

6 (30,36] 0.06 1060

7 (36,42] 0.04 1129

8 (42,48] 0.04 1456

9 (48,54] 0.03 1423

10 (54,60] 0.03 1138

11 (60,66] 0.04 829

12 (66,72] 0.03 555

13 (72,90] 0.02 358

ChildrenaredifficulttorecognizeLifelongreductioninfalserejection

FalseNon-MatchRate+/- 99%BootstrapCI

AgeGrou

p

Source:NISTFRVTMay2017

FRV

T-

FAC

ER

EC

OG

NIT

ION

VE

ND

OR

TE

ST-

VE

RIFIC

AT

ION

38

yisheng_000 yisheng_001 yitu_000

vcog_002 vigilantsolutions_001 vigilantsolutions_002 visionlabs_001 visionlabs_002 vocord_001 vocord_002

ntechlab_002 rankone_000 rankone_002 samtech_000 tongyitrans_001 tongyitrans_002 vcog_001

itmo_001 itmo_002 morpho_000 neurotechnology_000 neurotechnology_001 noblis_000 ntechlab_001

digitalbarriers_000 digitalbarriers_001 id3_001 id3_002 innovatrics_000 innovatrics_001 isityou_000

3divi_000 3divi_001 ayonix_000 camvi_001 cyberextruder_001 dermalog_002 dermalog_003

0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4

0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

False non−match rate (FNMR) +/− 99% bootstrap CI

reor

der(a

gebi

n, a

ge_o

rder

, col

or =

fmr_

nom

inal

)

fmr_nominal

0.0001

0.001

Figure 21: For the visa images, the dots show FNMR by age group for two operating thresholds corresponding to FMR = {0.001, 0.0001} computed over all O(1010)impostor scores. Given a pair of face images taken at different times, we assign a false non-match to the bin that is the arithmetic average of the subject’s ages. This plotshows only the effect of age, not ageing. The number of comparisons in each bin is generally in the thousands. However the FNMR for the first and last bins are eachcomputed over fewer than 150 comparisons.

2017/10/

1208:35:12

FNM

R(T)

“Falsenon-m

atchrate”

FMR

(T)“False

match

rate”

FRV

T-

FAC

ER

EC

OG

NIT

ION

VE

ND

OR

TE

ST-

VE

RIFIC

AT

ION

38







0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4

0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]


reor

der(a

gebi

n, a

ge_o

rder

, col

or =

fmr_

nom

inal

)

fmr_nominal

0.0001

0.001


2017/10/

1208:35:12

FNM

R(T)

“Falsenon-m

atchrate”

FMR

(T)“False

match

rate”

FRV

T-

FAC

ER

EC

OG

NIT

ION

VE

ND

OR

TE

ST-

VE

RIFIC

AT

ION

38







0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4

0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]


reor

der(a

gebi

n, a

ge_o

rder

, col

or =

fmr_

nom

inal

)

fmr_nominal

0.0001

0.001


2017/10/

1208:35:12

FNM

R(T)

“Falsenon-m

atchrate”

FMR

(T)“False

match

rate”

FRV

T-

FAC

ER

EC

OG

NIT

ION

VE

ND

OR

TE

ST-

VE

RIFIC

AT

ION

38







0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4

0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]

(0,4](4,10]

(10,16](16,20](20,24](24,28](28,32](32,36](36,40](40,48](48,56](56,64](64,72]

(72,120]


reor

der(a

gebi

n, a

ge_o

rder

, col

or =

fmr_

nom

inal

)

fmr_nominal

0.0001

0.001


2017/10/

1208:35:12

FNM

R(T)

“Falsenon-m

atchrate”

FMR

(T)“False

match

rate”

“Expected”FMRfromoldvs.youngimpostors

9

−2.1

−3.5

−4.7

−4.5

−4.4

−4.6

−4.8

−5.0

−2.6

−5.1

−5.3

−5.4

−6.0

−5.8

−3.2

−2.4

−3.1

−3.3

−3.4

−3.5

−3.7

−4.0

−2.5

−4.1

−4.4

−4.7

−5.1

−5.3

−4.4

−2.9

−2.9

−3.0

−3.0

−3.2

−3.3

−3.6

−3.7

−3.8

−4.2

−4.6

−5.2

−5.2

−4.2

−3.2

−3.0

−2.8

−2.6

−2.7

−2.9

−3.2

−3.9

−3.5

−3.9

−4.3

−5.0

−5.2

−4.2

−3.4

−3.1

−2.8

−2.4

−2.5

−2.7

−3.0

−3.9

−3.2

−3.6

−4.0

−4.7

−4.9

−4.5

−3.6

−3.3

−2.9

−2.6

−2.5

−2.7

−2.8

−4.2

−3.0

−3.4

−3.8

−4.3

−4.5

−4.8

−3.7

−3.5

−3.1

−2.8

−2.7

−2.7

−2.8

−4.4

−2.9

−3.1

−3.5

−4.0

−4.3

−5.0

−3.9

−3.7

−3.3

−3.0

−2.8

−2.8

−2.8

−4.5

−2.8

−3.0

−3.3

−3.7

−4.0

−2.4

−2.6

−3.7

−3.8

−3.7

−3.9

−4.1

−4.3

−2.2

−4.5

−4.8

−5.0

−5.4

−5.5

−5.2

−4.1

−4.0

−3.5

−3.2

−3.0

−2.9

−2.8

−4.6

−2.7

−2.7

−2.9

−3.2

−3.5

−5.3

−4.3

−4.3

−3.8

−3.4

−3.2

−3.1

−2.9

−4.8

−2.7

−2.6

−2.6

−2.8

−3.0

−5.2

−4.6

−4.7

−4.2

−3.8

−3.5

−3.4

−3.2

−5.0

−2.9

−2.6

−2.5

−2.5

−2.5

−5.3

−4.9

−5.2

−4.9

−4.4

−4.1

−3.8

−3.6

−5.1

−3.1

−2.7

−2.4

−2.3

−2.2

−5.3

−5.2

−5.7

−5.2

−5.0

−4.6

−4.2

−3.9

−5.1

−3.4

−2.9

−2.5

−2.2

−1.9

−1.8

−3.1

−4.2

−4.1

−3.8

−4.0

−4.2

−4.5

−2.2

−4.6

−4.8

−4.9

−5.9

−5.5

−2.8

−1.8

−2.4

−2.7

−2.6

−2.8

−3.0

−3.3

−2.0

−3.5

−3.9

−4.1

−4.6

−5.0

−4.0

−2.2

−2.1

−2.2

−2.1

−2.3

−2.5

−2.7

−3.2

−3.0

−3.4

−3.8

−4.4

−4.4

−3.5

−2.5

−2.3

−2.0

−1.7

−1.7

−2.0

−2.3

−3.3

−2.6

−3.0

−3.4

−4.2

−4.3

−3.5

−2.6

−2.3

−1.9

−1.5

−1.5

−1.7

−2.0

−3.3

−2.3

−2.6

−3.1

−3.8

−4.0

−3.8

−2.8

−2.5

−2.0

−1.6

−1.5

−1.7

−1.9

−3.6

−2.1

−2.4

−2.9

−3.5

−3.7

−4.2

−2.9

−2.7

−2.2

−1.8

−1.7

−1.8

−1.8

−3.8

−2.0

−2.2

−2.6

−3.1

−3.4

−4.5

−3.2

−2.9

−2.4

−2.0

−1.8

−1.8

−1.9

−3.9

−1.9

−2.1

−2.4

−2.8

−3.2

−2.1

−2.2

−3.2

−3.2

−3.0

−3.1

−3.4

−3.8

−1.8

−3.9

−4.2

−4.4

−4.9

−5.0

−4.8

−3.4

−3.2

−2.7

−2.2

−2.0

−1.9

−1.9

−4.1

−1.8

−1.9

−2.1

−2.4

−2.7

−4.8

−3.7

−3.5

−2.9

−2.5

−2.2

−2.1

−2.0

−4.3

−1.8

−1.7

−1.8

−1.9

−2.2

−4.8

−4.1

−4.0

−3.4

−2.8

−2.5

−2.4

−2.3

−4.5

−2.0

−1.7

−1.6

−1.7

−1.8

−5.2

−4.4

−4.5

−4.1

−3.5

−3.2

−2.9

−2.7

−4.6

−2.3

−1.9

−1.6

−1.5

−1.5

−5.2

−5.6

−5.2

−4.6

−4.2

−3.8

−3.4

−3.1

−4.7

−2.7

−2.1

−1.8

−1.5

−1.3

All impostor pairs Same sex and same region impostor pairs

(0,4]

(04,10

)

(10,16

]

(16,20

]

(20,24

]

(24,28

]

(28,32

]

(32,36

]

(36,40

]

(40,48

]

(48,56

]

(56,64

]

(64,72

]

(72,12

0](0,

4]

(04,10

)

(10,16

]

(16,20

]

(20,24

]

(24,28

]

(28,32

]

(32,36

]

(36,40

]

(40,48

]

(48,56

]

(56,64

]

(64,72

]

(72,12

0]

(0,4]

(04,10)

(10,16]

(16,20]

(20,24]

(24,28]

(28,32]

(32,36]

(36,40]

(40,48]

(48,56]

(56,64]

(64,72]

(72,120]

Age of enrollee

Age

of im

post

or

−6 −5 −4 −3 −2 −1log10 FMR

Cross age FMR at threshold T = 0.091 for algorithm ntechlab_000, giving FMR(T) = 0.001 globally.LowFMRDissimilar

HighFMRSimilar

Neutral

LowFMR HigherFMR

• FMR=0.001• TypicalinePassport Gates• Achievedbysettingathreshold,T• Determinedbya“large”empiricaltrial• ThresholdremainsfixedforALLtrials

1:1Impostors:Falsepositivesintheelderly• TfixedtogiveFMR=0.001

• But20-somethingsmatchwithFMR=0.01

• And30-somethingsmatchwithFMR=0.03

• But70-somethingsmatchwithFMR=0.05

• Nominal”1in1000”impostorchancehasx50securityvulnerability• Thisismassive

• Heterogeneitygives“large”varianceacrossages.

10

−2.1

−3.5

−4.7

−4.5

−4.4

−4.6

−4.8

−5.0

−2.6

−5.1

−5.3

−5.4

−6.0

−5.8

−3.2

−2.4

−3.1

−3.3

−3.4

−3.5

−3.7

−4.0

−2.5

−4.1

−4.4

−4.7

−5.1

−5.3

−4.4

−2.9

−2.9

−3.0

−3.0

−3.2

−3.3

−3.6

−3.7

−3.8

−4.2

−4.6

−5.2

−5.2

−4.2

−3.2

−3.0

−2.8

−2.6

−2.7

−2.9

−3.2

−3.9

−3.5

−3.9

−4.3

−5.0

−5.2

−4.2

−3.4

−3.1

−2.8

−2.4

−2.5

−2.7

−3.0

−3.9

−3.2

−3.6

−4.0

−4.7

−4.9

−4.5

−3.6

−3.3

−2.9

−2.6

−2.5

−2.7

−2.8

−4.2

−3.0

−3.4

−3.8

−4.3

−4.5

−4.8

−3.7

−3.5

−3.1

−2.8

−2.7

−2.7

−2.8

−4.4

−2.9

−3.1

−3.5

−4.0

−4.3

−5.0

−3.9

−3.7

−3.3

−3.0

−2.8

−2.8

−2.8

−4.5

−2.8

−3.0

−3.3

−3.7

−4.0

−2.4

−2.6

−3.7

−3.8

−3.7

−3.9

−4.1

−4.3

−2.2

−4.5

−4.8

−5.0

−5.4

−5.5

−5.2

−4.1

−4.0

−3.5

−3.2

−3.0

−2.9

−2.8

−4.6

−2.7

−2.7

−2.9

−3.2

−3.5

−5.3

−4.3

−4.3

−3.8

−3.4

−3.2

−3.1

−2.9

−4.8

−2.7

−2.6

−2.6

−2.8

−3.0

−5.2

−4.6

−4.7

−4.2

−3.8

−3.5

−3.4

−3.2

−5.0

−2.9

−2.6

−2.5

−2.5

−2.5

−5.3

−4.9

−5.2

−4.9

−4.4

−4.1

−3.8

−3.6

−5.1

−3.1

−2.7

−2.4

−2.3

−2.2

−5.3

−5.2

−5.7

−5.2

−5.0

−4.6

−4.2

−3.9

−5.1

−3.4

−2.9

−2.5

−2.2

−1.9

−1.8

−3.1

−4.2

−4.1

−3.8

−4.0

−4.2

−4.5

−2.2

−4.6

−4.8

−4.9

−5.9

−5.5

−2.8

−1.8

−2.4

−2.7

−2.6

−2.8

−3.0

−3.3

−2.0

−3.5

−3.9

−4.1

−4.6

−5.0

−4.0

−2.2

−2.1

−2.2

−2.1

−2.3

−2.5

−2.7

−3.2

−3.0

−3.4

−3.8

−4.4

−4.4

−3.5

−2.5

−2.3

−2.0

−1.7

−1.7

−2.0

−2.3

−3.3

−2.6

−3.0

−3.4

−4.2

−4.3

−3.5

−2.6

−2.3

−1.9

−1.5

−1.5

−1.7

−2.0

−3.3

−2.3

−2.6

−3.1

−3.8

−4.0

−3.8

−2.8

−2.5

−2.0

−1.6

−1.5

−1.7

−1.9

−3.6

−2.1

−2.4

−2.9

−3.5

−3.7

−4.2

−2.9

−2.7

−2.2

−1.8

−1.7

−1.8

−1.8

−3.8

−2.0

−2.2

−2.6

−3.1

−3.4

−4.5

−3.2

−2.9

−2.4

−2.0

−1.8

−1.8

−1.9

−3.9

−1.9

−2.1

−2.4

−2.8

−3.2

−2.1

−2.2

−3.2

−3.2

−3.0

−3.1

−3.4

−3.8

−1.8

−3.9

−4.2

−4.4

−4.9

−5.0

−4.8

−3.4

−3.2

−2.7

−2.2

−2.0

−1.9

−1.9

−4.1

−1.8

−1.9

−2.1

−2.4

−2.7

−4.8

−3.7

−3.5

−2.9

−2.5

−2.2

−2.1

−2.0

−4.3

−1.8

−1.7

−1.8

−1.9

−2.2

−4.8

−4.1

−4.0

−3.4

−2.8

−2.5

−2.4

−2.3

−4.5

−2.0

−1.7

−1.6

−1.7

−1.8

−5.2

−4.4

−4.5

−4.1

−3.5

−3.2

−2.9

−2.7

−4.6

−2.3

−1.9

−1.6

−1.5

−1.5

−5.2

−5.6

−5.2

−4.6

−4.2

−3.8

−3.4

−3.1

−4.7

−2.7

−2.1

−1.8

−1.5

−1.3


(0,4]

(04,10

)

(10,16

]

(16,20

]

(20,24

]

(24,28

]

(28,32

]

(32,36

]

(36,40

]

(40,48

]

(48,56

]

(56,64

]

(64,72

]

(72,12

0](0,

4]

(04,10

)

(10,16

]

(16,20

]

(20,24

]

(24,28

]

(28,32

]

(32,36

]

(36,40

]

(40,48

]

(48,56

]

(56,64

]

(64,72

]

(72,12

0]

(0,4]

(04,10)

(10,16]

(16,20]

(20,24]

(24,28]

(28,32]

(32,36]

(36,40]

(40,48]

(48,56]

(56,64]

(64,72]

(72,120]

Age of enrollee

Age

of im

post

or

−6 −5 −4 −3 −2 −1log10 FMR

Cross age FMR at threshold T = 0.091 for algorithm ntechlab_000, giving FMR(T) = 0.001 globally.

−2.1

−3.5

−4.7

−4.5

−4.4

−4.6

−4.8

−5.0

−2.6

−5.1

−5.3

−5.4

−6.0

−5.8

−3.2

−2.4

−3.1

−3.3

−3.4

−3.5

−3.7

−4.0

−2.5

−4.1

−4.4

−4.7

−5.1

−5.3

−4.4

−2.9

−2.9

−3.0

−3.0

−3.2

−3.3

−3.6

−3.7

−3.8

−4.2

−4.6

−5.2

−5.2

−4.2

−3.2

−3.0

−2.8

−2.6

−2.7

−2.9

−3.2

−3.9

−3.5

−3.9

−4.3

−5.0

−5.2

−4.2

−3.4

−3.1

−2.8

−2.4

−2.5

−2.7

−3.0

−3.9

−3.2

−3.6

−4.0

−4.7

−4.9

−4.5

−3.6

−3.3

−2.9

−2.6

−2.5

−2.7

−2.8

−4.2

−3.0

−3.4

−3.8

−4.3

−4.5

−4.8

−3.7

−3.5

−3.1

−2.8

−2.7

−2.7

−2.8

−4.4

−2.9

−3.1

−3.5

−4.0

−4.3

−5.0

−3.9

−3.7

−3.3

−3.0

−2.8

−2.8

−2.8

−4.5

−2.8

−3.0

−3.3

−3.7

−4.0

−2.4

−2.6

−3.7

−3.8

−3.7

−3.9

−4.1

−4.3

−2.2

−4.5

−4.8

−5.0

−5.4

−5.5

−5.2

−4.1

−4.0

−3.5

−3.2

−3.0

−2.9

−2.8

−4.6

−2.7

−2.7

−2.9

−3.2

−3.5

−5.3

−4.3

−4.3

−3.8

−3.4

−3.2

−3.1

−2.9

−4.8

−2.7

−2.6

−2.6

−2.8

−3.0

−5.2

−4.6

−4.7

−4.2

−3.8

−3.5

−3.4

−3.2

−5.0

−2.9

−2.6

−2.5

−2.5

−2.5

−5.3

−4.9

−5.2

−4.9

−4.4

−4.1

−3.8

−3.6

−5.1

−3.1

−2.7

−2.4

−2.3

−2.2

−5.3

−5.2

−5.7

−5.2

−5.0

−4.6

−4.2

−3.9

−5.1

−3.4

−2.9

−2.5

−2.2

−1.9

−1.8

−3.1

−4.2

−4.1

−3.8

−4.0

−4.2

−4.5

−2.2

−4.6

−4.8

−4.9

−5.9

−5.5

−2.8

−1.8

−2.4

−2.7

−2.6

−2.8

−3.0

−3.3

−2.0

−3.5

−3.9

−4.1

−4.6

−5.0

−4.0

−2.2

−2.1

−2.2

−2.1

−2.3

−2.5

−2.7

−3.2

−3.0

−3.4

−3.8

−4.4

−4.4

−3.5

−2.5

−2.3

−2.0

−1.7

−1.7

−2.0

−2.3

−3.3

−2.6

−3.0

−3.4

−4.2

−4.3

−3.5

−2.6

−2.3

−1.9

−1.5

−1.5

−1.7

−2.0

−3.3

−2.3

−2.6

−3.1

−3.8

−4.0

−3.8

−2.8

−2.5

−2.0

−1.6

−1.5

−1.7

−1.9

−3.6

−2.1

−2.4

−2.9

−3.5

−3.7

−4.2

−2.9

−2.7

−2.2

−1.8

−1.7

−1.8

−1.8

−3.8

−2.0

−2.2

−2.6

−3.1

−3.4

−4.5

−3.2

−2.9

−2.4

−2.0

−1.8

−1.8

−1.9

−3.9

−1.9

−2.1

−2.4

−2.8

−3.2

−2.1

−2.2

−3.2

−3.2

−3.0

−3.1

−3.4

−3.8

−1.8

−3.9

−4.2

−4.4

−4.9

−5.0

−4.8

−3.4

−3.2

−2.7

−2.2

−2.0

−1.9

−1.9

−4.1

−1.8

−1.9

−2.1

−2.4

−2.7

−4.8

−3.7

−3.5

−2.9

−2.5

−2.2

−2.1

−2.0

−4.3

−1.8

−1.7

−1.8

−1.9

−2.2

−4.8

−4.1

−4.0

−3.4

−2.8

−2.5

−2.4

−2.3

−4.5

−2.0

−1.7

−1.6

−1.7

−1.8

−5.2

−4.4

−4.5

−4.1

−3.5

−3.2

−2.9

−2.7

−4.6

−2.3

−1.9

−1.6

−1.5

−1.5

−5.2

−5.6

−5.2

−4.6

−4.2

−3.8

−3.4

−3.1

−4.7

−2.7

−2.1

−1.8

−1.5

−1.3


(0,4]

(04,10

)

(10,16

]

(16,20

]

(20,24

]

(24,28

]

(28,32

]

(32,36

]

(36,40

]

(40,48

]

(48,56

]

(56,64

]

(64,72

]

(72,12

0](0,

4]

(04,10

)

(10,16

]

(16,20

]

(20,24

]

(24,28

]

(28,32

]

(32,36

]

(36,40

]

(40,48

]

(48,56

]

(56,64

]

(64,72

]

(72,12

0]

(0,4]

(04,10)

(10,16]

(16,20]

(20,24]

(24,28]

(28,32]

(32,36]

(36,40]

(40,48]

(48,56]

(56,64]

(64,72]

(72,120]

Age of enrollee

Age

of im

post

or−6 −5 −4 −3 −2 −1

log10 FMR

Cross age FMR at threshold T = 0.091 for algorithm ntechlab_000, giving FMR(T) = 0.001 globally.

Source:FRVT2017using~200Kvisaimages.https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt-ongoing

FaceRecognitionFalseMatchOutcomes:EffectofCountry-of-Birth

Crosscountry-of-birtheffectsonFMR

0.01566

0.00006

0.00003

0.01085

0.00010

0.00004

0.01326

0.01141

0.00003

0.00056

0.00001

0.00897

0.02434

0.00000

0.00021

0.01311

0.00002

0.00001

0.02126

0.00008

0.00003

0.00015

0.00007

0.00001

0.00175

HAT

JPN

KOR

NRA

POL

HAT JPN KOR NRA POLCountry of birth of enrollee

Coun

try o

f birt

h of

impo

stor

−6 −5 −4 −3 −2 −1log10 FMR

Cross−country FMR at T = 30.260 for neurotechnology_000

• Nigeria– Korea LowFMR• Haiti– Poland LowFMR

• Poland– Poland FMR~TargetFMR

• Nigeria– Nigeria FMR=1in50• Nigeria– Haiti FMR=1in80

• Korea– Korea FMR=1in40

Impostorsaresame-sex,same-agegroupFMRnominal=0.001.


Doesdarkskincausefalsematches?

0.02249

0.01231

0.00017

0.00009

0.01005

0.01566

0.00022

0.00015

0.00009

0.00014

0.02198

0.01335

0.00000

0.00007

0.01130

0.01367

0.08905

0.03853

0.00012

0.00011

0.04045

0.05049

0.00004

0.00001

0.00002

0.00003

0.05205

0.01748

0.00000

0.00002

0.01295

0.01556

0.00423

0.00248

0.00037

0.00013

0.00163

0.00498

0.00052

0.00015

0.00002

0.00006

0.02219

0.01039

0.00000

0.00003

0.01026

0.01472

0.00517

0.00321

0.00018

0.00014

0.00240

0.00533

0.00036

0.00020

0.00004

0.00007

0.02105

0.01260

0.00001

0.00003

0.01041

0.01483

neurotechnology_000 ntechlab_001

tongyitrans_001 yitu_000

GHAN

HAT

IND

PKST

GHAN

HAT

IND

PKST

GHAN HAT IND PKST GHAN HAT IND PKSTCountry of birth of enrollee

Cou

ntry

of b

irth

of im

post

or

−6 −5 −4 −3 −2 −1log10 FMR

• IndiaandPakistangivehighFMRwithinregion

• HaitiandGhanagivehighFMRwithinregion

• BUT

• Butcrossregiondoesnotgivefalsematches.

• NEXT:• Measureskintone.• CheckISOcompliance7bits

ofgreyonface.


Summary

• Facerecognitionalgorithmsaresensitivetodemographics• Race>Age>Sex• Somealgorithmdependence

• Raceeffectslikelyduetotrainingdata• Opportunitiesformitigation

• Needtobepreciseaboutwhatthemetricis• Falsenegativevs.FalsePositive(vs.FailuretoCapture)

• 1:1Falsepositiveratespresentsecurityvulnerabilitiesduetodemographics• 1:NSystems

• Arelargelyuntestedfordemographics• Haveknownavenuesformitigationofdemographiceffects

• NISTwillauthorreportinlate2018ondemographics• DatafromFRVT2018– 1:NtestwithN>20million• Imageryfromimmigration:country-of-birthasproxyforrace• Imageryfromlawenforcement

Thanks

15

[email protected]@nist.gov

IARPA/NISTFaceRecognitionPrizeChallenge

Bias in Face Recognition: What does that even mean? … in Face Recognition: What does that even...

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Transcript of Bias in Face Recognition: What does that even mean? … in Face Recognition: What does that even...