Can Facial Uniqueness be Inferredfrom Impostor Scores?

Abhishek Dutta

Oct 14, 2013, Nijmegen, Netherlands.

Presentation accompanying the following paper:A. Dutta, R. Veldhuis and L. Spreeuwers. Can Facial Uniqueness be Inferred from Impostor Scores?, BTFS 2013,Nijmegen, Netherlands.

A quick question

Image Pair 1

Are they same subject ?

A quick question ...

Image Pair 2

What about this image pair?Are they same subject?

Facial Uniqueness (or, Distinctness) in a Population

Image Pair 1 Image Pair 2

person-id261

person-id263

person-id272

Irrespective of your decision {yes, no, maybe} , you were:I more uncertain in your decision for Image Pair 1,I less uncertain in your decision for Image Pair 2.

Why ?

Facial Uniqueness (or, Distinctness) in a Population ...

Image Pair 1 Image Pair 2

person-id261

person-id263

person-id272

If we assume that faces form a Gaussian cloud in a highdimensional space, then

I identity relates to direction, andI distinctness relates to distance from the mean.

Therefore, we are more confident in making decision about identitywhen facial images are well separated in the face space – by virtueof their distinctness (as in Image pair 2).

Facial Uniqueness (or, Distinctness) in a Population ...

I Therefore, in Biometrics, researchers have been activelyinvolved in measuring uniqueness from facial photographs

I Such facial uniqueness measurements are useful to build anface recognition system that adaptively vary the decisionthreshold to improve recognition performance.

Measuring Facial Uniqueness

Impostor Populationhow unique is

this facialimage? with respect to this

impostor population?

similarity score

coun

t

coun

t

similarity score

fairly commonface

highly distinctface

Measuring Facial Uniqueness ...

Impostor Populationhow unique is

this facialimage? with respect to this

impostor population?

similarity scoreco

unt

coun

t

similarity score

fairly commonface

highly distinctface

AssumptionI similarity score is influenced only by facial identity

I non-unique facial image1 will generally exhibit high level ofsimilarity to many other subjects in a large population (bydefinition)

Hypothesis facial uniqueness of a subject can be inferred from itsimpostor similarity score distribution

1lamb in the biometric zoo

Can Facial Uniqueness be Inferred from Impostor Scores?

In this paper, we investigate the stability of facial uniquenessmeasures based on impostor scores. For this, we perform followingtwo experiments:

Experiment 1

To study the influence of image quality variations on the impostorscore distribution.

Experiment 2

To study the stability of a recently proposed Impostor BasedUniqueness Measure (IUM) of [Klare and Jain 2013] under imagequality variations.

Exp. 1: Influence of Image Quality on Impostor Scores

Probe(Average Face)

Baseline Gallery

Motion Blur(angle = 0)

len. = 09 len. = 31

Gaussian Noise(mean= 0)

var. = 0.07 var. = 0.3

Pose(camera-id)

05_1 04_1 19_014_013_008_0 05_0

19_108_1

Gallery with Image Quality Variation

Baseline ImpostorScore Distribution

Quality based ImpostorScore Distribution

Exp. 1: Results

Pose Blur (Motion) Noise (Gaussian)

0.0

0.1

0.2

0.3

0.4

0

20

40

60

80

−0.1

0.0

0.1

0.2

−2

−1

0

1

2

3

FaceV

AC

SV

erilo

ok

LR

PC

AcLD

A

08

_1

08

_0

13

_0

14

_0

05

_1

05

_0

04

_1

19

_0

19

_1 0 3 5 7 13

17

29

31

0

0.0

07

0.0

3

0.0

7

0.1

0.3

Quality Variation [ Pose: camera−id | Blur: blur length, angle = 0 | Noise: variance, mean = 0 ]

Sim

ilari

ty s

co

re w

ith

ave

rag

e fa

ce

Pose Blur (Motion) Noise (Gaussian)

0.0

0.1

0.2

0.3

0.4

0

20

40

60

80

−0.1

0.0

0.1

0.2

−2

−1

0

1

2

3

FaceV

AC

SV

erilo

ok

LR

PC

AcLD

A

08

_1

08

_0

13

_0

14

_0

05

_1

05

_0

04

_1

19

_0

19

_1 0 3 5 7 13

17

29

31

0

0.0

07

0.0

3

0.0

7

0.1

0.3

Quality Variation [ Pose: camera−id | Blur: blur length, angle = 0 | Noise: variance, mean = 0 ]

Sim

ilari

ty s

co

re w

ith

ave

rag

e fa

ce

I the nature of impostor score distribution corresponding to allthree types of quality variations is significantly different fromthe baseline impostor distribution.

I the impostor score distribution also seem to be responding toquality variations.

Exp. 1: Results

Pose Blur (Motion) Noise (Gaussian)

0.0

0.1

0.2

0.3

0.4

0

20

40

60

80

−0.1

0.0

0.1

0.2

−2

−1

0

1

2

3

Fa

ce

VA

CS

Ve

riloo

kL

RP

CA

cL

DA

08

_1

08

_0

13

_0

14

_0

05

_1

05

_0

04

_1

19

_0

19

_1 0 3 5 7 13

17

29

31

0

0.0

07

0.0

3

0.0

7

0.1

0.3

Quality Variation [ Pose: camera−id | Blur: blur length, angle = 0 | Noise: variance, mean = 0 ]

Sim

ilari

ty s

core

with a

vera

ge face

I the impostor score distribution of the four systems respond ina different way to the three types of image quality variations.

Exp. 1: Conclusion

Impostor score distribution is not only influenced by identity(as expected) but also by the image quality like pose, blur andnoise.

Exp. 2: Stability of [Klare and Jain 2012] IUM

In this experiment, we study the stability of a recently proposedImpostor Based Uniqueness Measure (IUM) of [Klare and Jain2013] under image quality variations.

SmaxSmin

Smean

similarity score

count impostor score

distribution

IUM score =Smax − Smean

Smax − Smin

Uniqueness2 IUM score

high ∼ 1.0low ∼ 0.0

2with respect to the impostor population

Exp. 2: Stability of [Klare and Jain 2012] IUM ...

query imagefrom session 4

remaining 197 subjectsfrom session 3

remaining 197 subjectsfrom session 4

query imagefrom session 3

1006 subjects fromFERET Fa subset 1039 subjects from

CAS-PEAL pose PM+00

Impostor Population for Session 3 image

Impostor Population for Session 4 image

I We vary the quality of session 4 images (pose, noise, blur)

I If the IUM scores are stable with image quality variations, theIUM scores computed from session 3 and 4 should remainhighly correlated despite quality variation in session 4 images.

Exp. 2: Results

Pose Blur (Motion) Noise (Gaussian)

0.0

0.5

1.0

0.0

0.5

1.0

0.0

0.5

1.0

0.0

0.5

1.0

Fa

ce

VA

CS

Ve

riloo

kL

RP

CA

cL

DA

08_1

08_0

13_0

14_0

05_1

05_0

04_1

19_0

19_1 0 5 9 17

31

0

0.0

3

0.0

7

0.1

0.3

Quality Variation [ Pose: camera−id | Blur: blur length, angle = 0 | Noise: variance, mean = 0 ]

Norm

aliz

ed c

orr

ela

tion c

oeffic

ient

Imposter based Uniqueness Measure of [Klare and Jain 2012]is not stable under image quality variations.

Conclusion

I We have shown that impostor score is influenced by bothidentity and quality of facial images.

I We have also shown that any attempt to measurecharacteristics of facial identity (like facial uniqueness) solelyfrom impostor score distribution shape may give misleadingresults in the presence of image quality degradation in theinput facial images.

Future Work

I This research has thrown up many questions in need of furtherinvestigation regarding the stability of existing facialuniqueness measures based solely on impostor scores.

I More research is needed to better understand the impact ofimage quality on the impostor score distribution. Such studieswill help develop uniqueness measures that are robust toquality variations.

ReferencesI Brendan F. Klare and Anil K. Jain, Face recognition: Impostor-based

measures of uniqueness and quality, in Biometrics: Theory, Applicationsand Systems (BTAS), 2012 IEEE Fifth International Conference on, 2012,pp.237244.

I George Doddington, Walter Liggett, Alvin Martin, Mark Przybocki, andDouglas Reynolds, Sheep, goats, lambs and wolves: A statistical analysisof speaker performance in the nist 1998 speaker recognition evaluation, inProceedings of International Conference on Spoken Language Processing,1998.

I Cognitec Systems, FaceVACS C++ SDK Version 8.4.0, 2010.

I Neurotechnology, 2011. VeriLook C++ SDK Version 5.1,

I CSU Baseline Algorithms - Jan. 2012 Releases,http://www.cs.colostate.edu/facerec/ algorithms/baselines2011.php.

Questions and Feedback

http://abhishekdutta.org