On Testing the 

Robustness of 

Liveness Detection 

Feature Extractors 

for Fingerprint Live 

Scanners 

IBPC 2012, Gaithersburg, MD, USA

Patrick Schuch
Outline

Why liveness detection?

Typical Workflow

Known challenges

How to overcome 

An example 

Discussion & Conclusion 

2 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
Who is DERMALOG?

HQ in Hamburg, Germany

Outpost in Kuala Lumpur, Malaysia

Modality: fingerprints

Main Products: 

AFIS 

Live scanners 

3 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
Why Liveness Detection?

4 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Easy & 

cheap

Manuals 

available

Vulnerable 

systems 

Spoofing

False 

accepts

Loss of 

trust 

Loss of 

acceptance

Risk
Why Liveness Detection?

5 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Detect spoofing

Increase security

Increase customer’s trust

Increase acceptance
Typical Workflow

Liveness

score 

Classification

Feature extraction

Input data

6 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Grayscale 

images

Additional 

hardware 

measuring

Result: 

liveness

score 

Lab: good 

results on known 

data

Field: bad 

results on 

unknown data

Classifier 

overtrained? 
Known Challenges

7 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

External

• Illumination

• Temperature

• Humidity

• Dust/Dirt

• Latents

• Population (skin etc.)

• ... 

Internal

• Noise

• Defects

• Sharpness

• Fabrication

tolerances

• ... 

ISO/IEC 29197: Evaluation Methodology for 

Environmental Influence in Biometric Systems 

Performance 
Known Challenges

8 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Test single 

feature 

extractors vs. 

disturbances 

Reveal 

problems 

Solve 

problems / 

know 

limitations 
How to Test

9 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Know the 

challenges

Simulate 

challenges

Compute 

performance

Interpret 

results

Real data is expensive

Disturbances:

How to model them mathematically

What must be expected?
Analyzation Feature

Extraction Configuration

Data

Training 

Data

Testing 

Data

Reference 

Testing Data

Evaluation 

Testing Data

Reference 

Training Data

Classifier

Baseline 

Performance

Evaluation 

Performance

Reference 

Testing 

Features

Evaluation 

Testing 

Features

Reference 

Training 

Features

Disturbance

Training

Relative 

Performance

Confidence 

Interval

How to Test

10 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
Example - Data

LivDet2009 data set

Subset only

Identix DFR2100

686 dpi, 720x720 pixels

Cropped ROI

11 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Training 

data

750 

genuine

250 

gelatin

Testing 

data

3,000 

genuine

1,000 

gelatin
Example - Tested Feature Extractors

Kurtosis

• Proposed by Abhyankar et al. [2006]

• Baseline performance: EER ~ 21%

Coefficient of variation

• Proposed by Abhyankar et al. [2006]

• Baseline performance: EER ~ 8.9%

Surface coarseness

• Proposed by Moon et al. [2008]

• Baseline performance: EER ~ 25%

Combined

• Combination of the previous three

• Baseline performance: EER ~ 8.4%

12 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

γ2

μ

σ�
Example - Disturbances to be Tested

Illumination offset

• Internal/External illumination variation

• Homogenous offset

White Gaussian Noise

• Noise in components

Shot Noise

• A.k.a. “Salt and Pepper Noise”

• Defect pixels

Latents

• “Dirt” from previous acquisitions

• Simulated latents 

13 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
Example – Confidence Intervals

14 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Feature Extractor EER on 

Testing Set

Upper Bound for 

Relative FRR

Upper Bound for 

Relative FAR

Kurtosis 21.3% 1.07 1.12

Coefficient of variation 8.9% 1.12 1.22

Surface coarseness 25.0% 1.06 1.11

Combined 8.4% 1.13 1.23

Naive Bayes Classifier

False suspicious presentation detection rate (false SP-DR aka FRR)

False suspicious presentation non-detection rate (false SP-NDR aka FAR)

Use upper bounds of Confidence Intervals (significance level α = 0.05) 

Only relative error rates will be used
Example – Some Results

15 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Baseline performance for offset 0

Tendencies revealed in relative error rates

Apply confidence intervals

Crop to relative performance 1.0
Example – Some Results

Parameter range [-10 10]

Strong decay for coefficient of variation

16 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
Example – Some Results

Parameter: Rate of defect pixels

“Pepper Noise”: All seem to be robust.

17 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
Example – Some Results

Parameter: Rate of defect pixels

“Salt Noise”: Kurtosis is not robust.

18 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
Example – Some Results

19 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Parameter range [0 4]

All extractors seem to be robust.
Example – Some Results

20 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Extractor: Kurtosis

Parameter range: mean [0 100], deviation [0 15]

Decay when mean gets close to common ridge mean.
Example – Some Results

Combination tested

Single extractor may 

have impact on 

combined 

performance.

21 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
Example – Some Results

Combination tested

Single extractor may 

have impact on 

combined 

performance.

22 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
Example – Some results

23 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Revealed problem: 

Kurtosis vs. “Salt 

Noise”

Solution: apply 

special filter

Robustness gained

Iterative process
Discussion

Testing simulations...

shall not replace real data.

is cheaper.

is better than no testing.

Curse of dimensionality: testing all combinations

Classifiers influence needs inspection.

24 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
Summary

Proposed method:

Easy workflow

Concentration on feature extractors

Know and model the environment

Tendencies observable in relative error rates.

Not limited to fingerprint

Weakest link may have serious impact

25 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
Outlook

Revealed weaknesses in our own extractors 

Robustness gained

Performance stabilized in the field

Robustness over performance

More disturbances to be modeled 

More sophisticated models

26 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH
On Testing the Robustness of Liveness Detection Feature 

Extractors for Fingerprint Live Scanners 

27 | Patrick Schuch | © 2012 DERMALOG Identification Systems GmbH

Do you have any questions? 

 Thank you.

Patrick Schuch

DERMALOG Identification Systems GmbH

Hamburg, Germany

patrick.schuch@dermalog.com