Evaluation of Liveness or 

Anti-spoofing in Biometric Systems 

Stephanie Schuckers, Andy Adler 

Bozhao Tan, Aaron Lewicke, Peter Johnson, Joe 

Sherry, David Yambay, Rachel Wallace, Greta Collins, 

Dominic Grimberg 

Funding provided by National Science Foundation, Dept. of 

Homeland Security, and the Center for Identification 

Technology Research (CITeR) 

The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 
Spoofing 

• Spoofing: “The process of defeating a 

biometric system through the introduction of 

fake biometric samples. 

• Artificially created biometrics: 

– lifted latent fingerprints 

– artificial fingers 

– image of a face or iris 

– high quality voice recordings 

– worst case—dismembered fingers 

• Famous ‘gummy fingers’ by Matsumoto 2002 

• Mythbusters episode in 2007 

• Spoof attack in early 2009 at Japanese 

border by a Korean woman 

1 Thalheim, et al, C’T article, 2002. 

The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 
Biometric Spoofing in 

Popular Media 

Cameron Diaz, Charlies Angels 

Tom Cruise, Minority Report 

Mythbusters, 2007 

The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 
Spoofing versus Obfuscation 

• Spoofing—posing as another individual 

– Positive identification applications 

• Obfuscation—hiding your identity 

– Negative identification applications 

– May form ‘new’ identity for positive identification 

– Mutilation of fingerprint 

– Texture-contact lens to hide iris pattern 

– Theatre makeup/putty to change facial characteristics 

The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 
Spoofing Techniques in our Lab 

• Dental materials for casts 

• Cooperative, high quality 

casts 

• Mold made from cast, also 

termed ‘replica’, ‘spoof’, 

‘fake finger’ 

• Materials for Mold: PlayDoh, gelatin, silicon rubber, 

paint, caulk, wood glue, 

paper, latex rubber, paper 

• Cadaver fingers 

The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Example Photos of Spoof Fingers 

and Resulting Images 

Caulk Paint Playdoh Silicon 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Spoof Techniques in our Lab

• Uncooperative 

• Lifted latent print, stolen 

fingerprint image 

• Fingerprint mask generation 

• Print on transparent film 

• Expose negative photosensitive 

silicon wafer 

• Develop to form cast 

• Pour silicone or other liquid 

material to form mold 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Liveness Detection 

• Also termed 

– ‘Vitality Detection’ 

– ‘Anti-Spoofing’ 

• Definition: to determine if the biometric being captured is an 

actual measurement from the authorized, live person who is 

present at the time of capture 

• “It is ‘liveness’, not secrecy, that counts,” Dorothy Denning 

– Your fingerprint is NOT secret. 

– Cannot reasonably expect it to be 

– Therefore, must ensure measurement is of the ‘real’ biometric and not a 

replica. 

– True for most other biometrics, with some exceptions to be discussed 

• Typically treated as a two class problem—live or spoof 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Liveness Detection 

• Rarely does biometric sensor measure 

‘liveness’, that is, liveness is not necessary

to measure the biometric 

• Hardware-based 

– Requires specialized hardware design 

– Integrated with biometric sensor 

• Software-based 

– Uses information already measured from 

biometric sensor 

– Additional processing needed to make a 

decision

• Liveness inherent to biometric 

– Must be ‘live’ to measure it, e.g., 

electrocardiogram 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

False Reject Rate 

Match NonMatch 

Match 99 1 

Non 

Match 

Matching 

Algorithm 

100 matched pairs, 

that is, 

100 individuals, 

each with 2 images 

100 genuine 

comparisons 

False Reject 

Rate= 

1 

99+1 

=0.01 

Truth 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

False Accept Rate 

Match NonMatch 

Match 99 1 

NonMatch 10 9990 

Matching 

Algorithm 

Each image compared 

With images from 

A different individual 

200*198 

imposter 

comparisons 

False Accept 

Rate= 

10 

10000 

=0.001 

No information regarding false accepts 

based on deliberate attempts to 

defeat the system 

Truth 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Now Anti-Spoofing--FRR 

Live Spoof 

Live 90 10 

Spoof 

Anti-spoofing 

Algorithm 

Typically based 

On a single image 

Assume ‘enrolled’ 

fingerprints are 

live 

100 anti-spoofing 

decisions 

False Reject 

Rate= 

10 

90+10 

=0.1 

Truth 

Spoof 

or 

Live? 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

FRR—Matching and Anti-Spoofing 

Live Spoof 

Live 90 10 

Spoof 

Anti-Spoofing 

Algorithm 

False Reject 

Rate= 

10 

90+10 

=0.1 

(10%) 

Match NonMatch 

Match 99 1 

Non 

Match 

Matching 

Algorithm 

False Reject 

Rate= 

1 

99+1 

=0.01 

(1%) 

Matcher Anti-Spoofing 

Detection 

Fusion ? 

Anti-Spoofing will 

Impact overall system 

Performance 

SystemFRR<=0.11 (11%) 

Truth 

Truth 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Now Anti-Spoofing-FAR?? 

Anti-spoofing 

Algorithm 

s 

Live Spoof 

ruth 

Live 90 10 

T

Spoof 

How to test? What is performance?

Need spoof dataset. 

Need common terminology— 

SpoofFAR?? 

Live Matched Pairs 

Spoof image

From same

individuals
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Performance Vocabulary 

• Biometric performance terminology 

– False reject rate—Error associated with rejecting an ‘genuine’ 

user 

– False accept rate—Error associated with accepting an unauthorized, ‘imposter’ user 

• Zero-effort attempt—no willful attempt 

• Anti-spoofing terminology 

– Live false reject rate—similar to above, now anti-spoofing 

detection algorithm may reject ‘genuine’ authorized user 

– Spoof false accept rate—error associated with accepting the 

presentation of a spoof 

• Non-zero effort attempt—willful attempt 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

System Level Performance 

Match 

Live 

Match 

Spoof 

Non-Match 

Live 

Non-Match 

Spoof 

Match 

Live 

Match 

Spoof 

Non-match 

Live 

Non-match 

Spoof 

Accept 

Reject 

Reject 

Reject 

SFAR 

LFRR FRR FRR/LFRR 

Poor Quality 

Spoof---Reject 

by Matcher 

Poor Quality 

Spoof---Reject 

by both 

FAR 

What do we call the remaining? Do we care? 

Truth 

System System Performance 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Software-based Fingerprint 

Liveness Detection 

• Live and spoof fingerprint images have 

distinctive characteristic images. 

• Utilizes fingerprint images from device 

• Examples 

– Perspiration pattern 

– Ridge/valley characteristics 

– Power spectrum 

– Skin deformation/Elasticity 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Perspiration Pattern

• Characterizes changes in 

perspiration pattern 

• Two methods: Wavelet Image, 

Ridge signal changes 

• Uses two or more images 

collected in series 

• Published reports use images 

with minimum time of two 

seconds 

 Images Wavelet analysis Perspiration Pattern 

Spoof Live Cadaver 

Derakhshani, et al, Pattern Recog, 2003 

Parthasaradhi, et al, IEEE SMC, 2005 

Abhyankar, et al, SPIE, 2004 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Perspiration Pattern

• Characterizes changes in 

perspiration pattern 

• Two methods: Wavelet Image, 

Ridge signal changes 

• Uses two or more images 

collected in series 

• Published reports use images 

with minimum time of two 

seconds 

 Images Wavelet analysis Perspiration Pattern 

Spoof Live Cadaver 

Parthasaradhi, et al, IEEE SMC, 2005 

Abhyankar, et al, SPIE, 2004 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Perspiration Pattern

• Dynamic: Depends on more than one image 

• Delay: May need a noticeable time delay 

• Variability in live fingers over populations 

• Variability due to environmental conditions (hot/cold) 

• Variability across to Sensors 

 Images Wavelet analysis Perspiration Pattern 

Spoof Live Cadaver 

Fingerprint Ridge Signal 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

2D Fourier transform 

•Coli et al, IEEE Workshop AutoID 2007 

•Jin, et al, Lecture Notes in CS, 2007

• Band-selective Fourier spectrum approach analyses the difference 

in spectral energies 

• Two similar methods 

• Relies on differences in ridge valley structure between live and 

spoofs 

• Sensitive to the sensor being used 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Ridge/Valley Characteristics 

 Ridge Signal 

Valley Signal 

• Relies on differences in 

ridge/valley structure 

between live and 

spoofs 

• Uses features 

measured from ridges 

and valleys separately 

• Sensitive to the sensor 

being used 

• Impacted by 

environmental 

conditions 

• Must represent large 

diversity in both spoof 

and live images 

Tan, et al, CVPR, 2006 

Ulchida, et al, LN in CS, 2004 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Skin Distortion or Deformation 

• Two methods 

• Chen et al: 

– Examines deformation 

pattern of a live finger on a 

scanner surface compared 

to that of a spoof image 

based on thin-plate splines 

– Single image 

• Antonelli et al: 

– Requires specific 

movement of finger on 

scanner with a minimum 

rotational speed for 

about 1 second 

– Multiple images

The non-linear def. model G (1st row) and the linear

+non-linear def. model F (2nd row) over the grid P 

using live and fake queries.

•Yi Chen, Anil Jain, and Sarat Dass, et. al 

•A. Antonelli, et al, IEEE TIFTS, 2006 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Environmental Impact 

 Same Individual 

With varying temperature and humidity 

• Varying 

temperature/

humidity situations 

• Indoor and outdoor 

collection 

• Multiple visits 

• Anti-Spoofing must 

be robust to 

varying 

fingerprints within 

an individual 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Optical 1 

Optical 2 

Capacitive 1 

Capacitive 2 

Same spoof material 

Different scanners 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Hardware-based Fingerprint 

Liveness Detection 

• Hardware-based 

– Temperature 

– Pulse 

– Blood pressure 

– Odor 

– Electrocardiogram 

– Multispectral imaging, spectroscopy 

• The Lumidigm J110 

Anti-Spoof scanner 

• MultiSpectral imaging 

Odor 

Electrocardiogram 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Electrocardiogram as a Biometric 

Wubbeler, Gerd. “Verification of Humans using 

the Electrocardiogram.” 26 June 2009. 

Single heart beats from three ECG 

recordings of one subject performed 

during 4 months 

Two separate subjects ECG readings 

• Electrical measurement of the 

heart from the surface of the 

body 

• E.g. two hands touching 

• Requires two points of 

contact on opposite side of 

the heart 

• Delay of at least one cycle of 

heart (>1s) 

• Privacy concerns—contains 

medical information 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Liveness Detection based on Fine 

Movements of the Fingertip Surface 

• Laser measures the small 

changes in the skin due to 

the expansion and 

contraction of papillary 

lines 

• Based on motion from 

cardiac cycle 

• Delay of at least one cycle 

of heart (>1s) 

• Needs to be evaluated 

when real fingerprint is 

integrated spoof finger 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Fake Fingerprint Detection 

by Odor Analysis 

• Odor sensor (electronic nose) 

to sample the odor signal 

• Discriminates spoofing 

materials 

• Integration of hardware may 

be bulky and expensive 

• Relies of knowledge of 

spoofing material which may 

be used 

Baldisserra et al, Advances in Biometrics, 2005. 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Finger Vein Biometrics 

M2-FV Finger Vein Reader Miura, et al, 2009. 

• Measures finger vein pattern 

Uses vein pattern as 

biometric 

Liveness is an inherent 

component of measuring the 

biometric 

Requires new hardware 

Requires stored template 

based on vein 

Are there ways to spoof this 

type of system? 

•

•

•

•

•
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Multispectral 

• MultiSpectral imaging with 

varying illumination and 

polarization 

• Commercial system which 

protects from spoofing 

• Hardware approach 

• Tradeoff—larger and more 

expensive 

The Lumidigm J110 AntiSpoof scanner 

Rowe et al. Advances in Biometrics, 2008, 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Commercial Products Emerging 

to Address Anti-Spoofing 

• M2SYS-M2-S1 

Swipe Reader 

“..will simply not read 

gummy fingers or 

other methods used 

to spoof fingerprint 

readers.”

•

• Atmel’s Thermal 

Swipe Sensor and 

Ekey’s software, 

Ekey states “near 

impossible to spoof 

the scanner.” 

•
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Factors in Fingerprint Liveness 

• Static or Dynamic 

– Static: Based on one image 

– Dynamic: Based on two or more images 

• Time delay 

– May require noticeable time delay 

– Measurements based on heart cycle will lead to delay as wait for one or 

more cycles >1s 

• User-assisted 

– Something additional the user must do to assist 

– Electrocardiogram requires two points of contact 

• Device dependence 

– Features may vary from one sensor to another 

– Hardware methods are difficult to update, each uses own technology 

• Environmental impact 

– Features may be impacted by environmental changes 

– Hot: more smudgy 

– Cold: dry 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Liveness Methods Impact on 

Standard Biometric Characteristics 

• Ease of Use 

– Dynamic, time delay 

– User assisted 

• Collectability 

– User assisted

• User acceptance 

– Measurement which requires medical information may not be 

acceptable to individuals 

• Universality 

– Perspiration differences may not be measurable in some 

individuals 

– Some individuals require lotion for fingerprint 

• Permanence 

– Environmental impact 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Biometric Characteristics 

• Uniqueness 

– Typically posed as a two class problem: Live or Spoof 

• Spoof Vulnerability 

– Need to assess liveness algorithms for their vulnerability

– Sensitivity to training set 

– May depend on ‘live’ features present only in live, 

independent of spoof, e.g. heart beat 

– May depend on differences between live and spoof 

images 

• unknown what the differences will be for materials 

that have not been used in training algorithm 

– Reality 

• Algorithms require a comparison 

• Relative to what? 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Characteristics for 

Liveness 

• As in biometrics, must consider many factors for 

liveness when integrating into overall system 

Adler, Schuckers, in Encyclopedia of Biometrics, 2009 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Software Based Liveness 

Algorithm Characteristics

H High; M Medium; L Low; - not applicable 

Ease of  Collect‐ User  Univers‐ Perman‐ Unique‐ Spoof 

use  ability  acceptanc ality  ence  ness  vulner‐

e  ability 

Perspira:on  H  H  H  M  M  L  M 

Deforma:on  L  L  H  M  ‐  ‐  M 

Power  H  H  H  M  H  L  H 

Spectrum 

Ridge‐valley  H  H  H  M  H  L  H 

profile 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Liveness Algorithm Performance Comparison 

Algorithm  No. Spoofs  No. Live  No.  No.  Live  Spoof 

impression frames  Performance  Performance 

s 

Perspira:on  18  18  1  2  88.89%  88.89% 

with Fourier 

space 

Surface  10 gela:n  23  1  1  100%  100% 

coarseness  24 plas:c 

clay 

Distor:on  40 (10  45 (2  10  20  88.76%  88.76% 

Analysis  silicone, 10  fingers) 

gela:n, 10 

latex, 10 

wood glue) 

Perspira:on  80  58  1  1  80% ‐ 100%   80% ‐ 100%  

with wavelet 

space 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

39

Dataset Sensors Model No. Resolution 

(dpi) 

Image size Live 

Samples 

Spoof 

Samples 

#1 CrossMatch Verifier 500 480x640 1500 1500 

300LC 

#2 Identix DFR2100 686 720x720 2000 2000 

#3 Biometrika FX2000 569 312x372 2000 2000 

The largest public fingerprint liveness dataset 

• Detection approaches tested on their home-made

live and spoof databases. 

No common dataset 

First liveness detection competition at ICIAP 

2009 with a public liveness database 

Collaborating with Univ. of Cagliari 

Focusing on software-based fingerprint liveness 

•

•

•

•

Liveness Detection 

Competition—LivDet 2009 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Ferrfake and Ferrlive 

error rates 

Ferrfake  rate of misclassified fake fingerprints (false acceptance) 

Ferrlive  rate of misclassified live fingeprints (false rejection) 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Importance of modeling 

individual variability for live 

subjects 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Next Steps

• Need common terminology for assessing anti-spoofing 

• Liveness detection or anti-spoofing will impact overall 

performance of biometric system 

• Must consider many characteristics when choosing 

liveness (ease of use, collectability, universality, etc.) 

• Public datasets can accelerate improvement 

• Need for multi-entity approach to testing 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

Next LivDet in 2011? 

• System (hardware/software) testing 

• Solutions depend on sensor/finger interaction (even 

software) 

• Anti-spoofing methods depend on strength of data used 

to design them 

• Testing requires collaboration of university/ government/

industry 

• Multiple entities attempting to spoof systems 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

References 

• C. Jin, H. Kim, S. Elliot, “Liveness Detection of Fingerprint Based on Band-Selective 

Fourier Spectrum,” Lecture Notes in Computer Sciences, vol. 4817, pp. 168-179, 

2007. 

• K. Uchida, “Image-Based Approach to Fingerprint Acceptability Assessment,” 

Lecture Notes in Computer Sciences, pp. 294-300, 2004. 

• A. Antonelli, R. Cappelli, D. Maio, D. Maltoni, “Fake Finger Detection by Skin 

Distortion Analysis,” IEEE Transactions on Information Forensics and Security, vol. 

1, no. 2, pp. 360-373, September 2006. 

• J. Jia, L. Cai, “Fake Finger Detection Based on Time-Series Fingerprint Image 

Analysis,” Lecture Notes in Computer Sciences, vol. 4681, pp. 1140-1150, 2007. 

• Parthasaradhi S, Derakhshani R, Hornak L, Schuckers SAC, Time-Series Detection 

of Perspiration as a Liveness Test in Fingerprint Devices, IEEE Transactions on 

Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 35, pp. 

335- 343, 2005. 

• B Tan, S Schuckers, Liveness Detection for Fingerprint Scanners Based on the 

Statistics of Wavelet Signal Processing, Computer Vision and Pattern Recognition 

Workshop, 2006 Conference on, Page(s):26 – 26, 17-22 June 2006. 

• Baldisserra, Denis, Annalisa Franco, Dario Maio, and Davide Maltoni. “Fake 

Fingerprint Detection by Odor Analysis ,.” In Advances in Biometrics, 265-272, 2005. 
The Center for Identification Technology Research 

An NSF I/UCR Center advancing integrative biometrics research www.citer.wvu.edu 

References--continued 

• C. Jin, H. Kim, S. Elliot, “Liveness Detection of Fingerprint Based on Band-Selective 

Fourier Spectrum,” Lecture Notes in Computer Sciences, vol. 4817, pp. 168-179, 

2007. 

• Miura, Naoto. “Feature Extraction of Finger Vein Patterns Based on Iterative Line 

Tracking and Its Application to Personal Identification.” 29 June 2009. 

• Wubbeler, Gerd. “Verification of Humans using the Electrocardiogram.” 26 June 

2009. 

• Rowe, Robert K., Paul W. Butler, and Kristin A. Nixon. "Multispectral Fingerprint 

Image Acquisition." Advances in Biometrics, 2008. 

• Andy Adler, Stephanie Schuckers, Security and Liveness: Overview, in 

Encyclopedia of Biometrics, editor: Stan Li, Springer Reference, 2009. 

• Stephanie Schuckers, Liveness: Fingerprint, in Encyclopedia of Biometrics, editor: 

Stan Li, Springer Reference, 2009 .