Biometric Liveness Detection: 

Framework and Metrics

Peter Johnson1, Richard Lazarick2, Emanuela Marasco4, Elaine 

Newton3

, Arun Ross4, Stephanie Schuckers1

1Clarkson University

2Computer Sciences Corporation (CSC)

3National Institute of Standards and Technology (NIST)

4West Virginia University

Funding provided by 

National Institute of Standards and Technology (NIST), National Science 

Foundation (NSF), Dept. of Homeland Security (DHS), and the Center for 

Identification Technology Research (CITeR) 

Presented at International Biometric Performance Conference (IBPC)

March, 2012
This Talk

• Categories of Subversive Presentation Attacks

• Performance Metrics for Suspicious Presentation 

Detection Systems

• Relationship between Liveness Detection and 

Challenge-Response
Subversive Presentation*

Live

Capture

Subject

Artefact

(e.g., fake finger, patterned contact, 

face photo)

NonSubversive

Presentation

*Some cases may also not be deliberate attacks 

(e.g., patterned contact for cosmetic reasons, non-conformant 

due to improper use of system, etc.)

*A detection system cannot infer intent, therefore, is called 

Suspicious Presentation Detection System

ARTIFICIAL 

Altered 

(e.g., mutilated finger, 

surgical alteration)

Nonconformant

(e.g., facial expression changes, 

side of finger)

Conformant

(e.g., zero-effort attack)

Coerced

(e.g., unconscious)

HUMAN 

Cadaver

(e.g., dismembered fingers)
Introduction—Definitions 

• Subversive Presentation

– Presentation of human or artificial biometric characteristics to the 

biometric capture subsystem in a fashion that interferes with or 

undermines the correct or intended policy of the biometric system. 

• Suspicious Presentation

– Presentation of a human or artificial characteristic to the biometric 

capture subsystem in a fashion that could interfere with the intended 

policy of the biometric system 

• Suspicious Presentation Detection (SPD)

– Automated determination of a suspicious presentation.

• Examples of SPD

– Liveness detection failure

– Artefact detection 

– Altered biometric detection

– Others terms that have been used: anti-spoofing, biometric fraud, spoof 

detection, authenticity detection, etc.
Subversive Presentation*

Artefact

(e.g., fake finger, patterned contact, 

face photo)

Altered 

(e.g., mutilated finger, 

surgical alteration)

Nonconformant

(e.g., facial expression changes, 

side of finger)

Conformant

(e.g., zero-effort attack)

Coerced

(e.g., unconscious)

NonSubversive

Presentation ARTIFICIAL HUMAN 

Cadaver

(e.g., dismembered fingers)

Artefact Detection

Live

Capture

Subject
Subversive Presentation*

Artefact

(e.g., fake finger, patterned contact, 

face photo)

Altered 

(e.g., mutilated finger, 

surgical alteration)

Nonconformant

(e.g., facial expression changes, 

side of finger)

Conformant

(e.g., zero-effort attack)

Coerced

(e.g., unconscious)

NonSubversive

Presentation ARTIFICIAL HUMAN 

Cadaver

(e.g., dismembered fingers)

Liveness Detection

Also helps 

with this

Live

Capture

Subject
Subversive Presentation*

Artefact

(e.g., fake finger, patterned contact, 

face photo)

Altered 

(e.g., mutilated finger, 

surgical alteration)

Nonconformant

(e.g., facial expression changes, 

side of finger)

Conformant

(e.g., zero-effort attack)

Coerced

(e.g., unconscious)

NonSubversive

Presentation ARTIFICIAL HUMAN 

Cadaver

(e.g., dismembered fingers)

Altered Biometric Detection

Live

Capture

Subject
Categories for Subversive Presentation 

Attacks
Categories for Subversive 

Presentation Attacks

• First step in development of scientific framework to evaluate suspicious

presentation detection security systems

• Classification and brief description of known attack types on biometric 

authentication at the sensor

• Provide foundation for development of effective countermeasures

– Basis for performance assessment

– Empirical testing of countermeasure effectiveness against known attacks

• Not a recipe book for creating artificial biometric traits

• Procedure to create an artificial subversive presentation characteristic:

– Source of biometric characteristic – Obtain information to describe characteristic

– Production of artefact – Process for creating artefact to present characteristic to sensor

• Human – no artificial characteristics used
Source of Biometric Characteristics

• Cooperative

– Characteristic captured directly from 

individual with assistance (e.g. 

finger mold, hand mold, face mask)

• Latent

– Characteristic captured indirectly 

through latent sample (e.g. latent 

fingerprint, latent palmprint, hair, 

skin, body fluid)

• Recording

– Characteristic captured directly from 

individual onto media (e.g. 

photograph, video recording, audio 

recording)

Coli, et al, 2006.
Source of Biometric Characteristics

• Template Regeneration

– Regenerate characteristic from 

template (e.g. fingerprint 

regeneration, face)

• Synthetic

– Synthetic characteristic, not mapped 

to real person (e.g. synthetic 

fingerprint, iris, face, voice, wolf 

synthesized sample)

• Impersonation

– Conversion of natural characteristic 

to another individual’s with artificial 

assistance (e.g. computer assisted 

voice)

Feng and Jain, Advances in Biometrics article, 2009. 
Production of Artefact

• Mold/cast 

– Create 3D representation of characteristic 

(negative)

– Cast is reproduction created from mold 

(e.g. theatrical face mask, finger artefact 

of modeling clay, gelatin, silicone, latex, 

wood glue, glycerin, etc.)

• Mask – modify or conceal 

characteristics (partially or 

completely) with artefact
Production of Artefact

• Direct rendering

– Printing 2D (e.g. photo of iris or face, 

fingerprint printed on transparency/paper)

– Printing 3D (e.g. contact lens printed with 

pattern, prosthetic hand printed with vein 

pattern)

– Etching (e.g. fingerprint etched on metal)

– Painting – patterns and colors painted on 

prosthesis

• Digital Media

– Computer screen – laptop or tablet to 

present image or video

– Audio – recording of voice

Thalheim, et al, C’T article, 2002.

Lefohn, et al, IEEE Computer Graphics & Applications article, 2003.

Seelen, “Countermeasures Against Iris Spoofing with Contact Lenses,” Iridian Technologies Inc.
Categories of Human Subversive 

Presentations (Non-Artefact Methods)

• Lifeless

– Cadaver

• Altered

– Mutilation (e.g. scarring, amputation, acid)

– Surgical modification (e.g. new fingerprint, 

nose job, face lift)

• Non-Conformant

– Impersonation (e.g. voice mimicry, forged 

signature)

– Presentation (e.g. hand shape control, 

facial expression/extreme, tip of side of 

finger)

• Conformant

– Zero effort impostor attempt (e.g. any 

normal presentation)

• Coerced

– Unconscious or under duress

Feng, et al, IEEE TIFS article, 2009. 
Performance Metrics for Suspicious

Presentation Detection Systems
State of Artefact Detection Performance 

Metrics

• Performance metrics for biometric systems – adapted 

unmodified for artefact detection assessment

– Classification rate (percent correctly classified)

– FAR/FMR – false accept rate/false match rate

– FRR/FNMR – false reject rate/false non match rate

– TAR/GAR – true accept rate/genuine accept rate

– EER – equal error rate

– ROC – receiver operating characteristic

– DET – detection error trade-off

• Need to distinguish “false accepts” in matching from 

“false accepts” in artefact detection

– Need common set of vocabulary
Evaluation of suspicious presentation 

detection systems

• The ability to correctly identify suspicious presentation 

attacks is quantified by a dedicated set of performance 

metrics

• The suspicious presentation detection error rates are 

defined based on the specific purpose of the suspicious

presentation detection module:

– E.g., live vs non-live, altered vs non-altered, artefact vs

non-artefact, etc.

– Performance metrics are confined to the defined goal

• Metrics for assessing suspicious presentation detection 

detection performance differ from those used for assessing 

matching performance 
General Model for Performance Evaluation 

• Suspicious Presentation Detection: When the system states 

that the presentation characteristic is suspicious 

• Non-Suspicious Presentation Detection: When the system 

states that the presentation characteristic is not suspicious

• Metrics for error cases:

– False Non-Suspicious Presentation Detection 

(FNSPD): a suspicious presentation is incorrectly 

classified as being a non-suspicious presentation

– False Suspicious Presentation Detection (FSPD): a 

non-suspicious presentation is incorrectly classified as 

being a suspicious presentation
Artefact Detection Case

• Goal: Evaluation of module that is designed to distinguish the 

presentation of an artefact from a non-artefact

– Artefact Detection: When the system states that the 

presentation characteristic is an artefact

– Non-Artefact Detection: When the system states that the 

presentation characteristic is not an artefact

• Metrics for error cases:

– False Artefact Detection Rate (FADR): proportion of nonartefact presentations incorrectly classified as being artefacts

– False Non-Artefact Detection Rate (FNDR): proportion of 

artefact presentations incorrectly classified as being nonartefacts
Traditional Metrics for Biometric 

Evaluation (Live Finger Input)

Segmentation Biometric Feature Extraction

Capture Sensor

Live Finger Presentation

Biometric 

Characteristics

Data Capture 

Subsystem

Signal Processing 

Subsystem

Quality Check

Artefact Detection

Module

Reference 

Creation

Data Storage Subsystem

Enrollment Database

Biometric Claim

Comparison Subsystem

Comparison

Match?

Decision Subsystem

Comparison Score

Reference Reference

Probe

Reject

Failure To 

Acquire 

Failure To Enroll

Reject

False Reject

False Accept

Decision (Reject/Accept)

Suspicious Presentation 

Detection Subsystem
Additional Metrics (Artefact Input)

Segmentation

Feature Extraction

Biometric

Capture Sensor

Biometric 

Characteristics

Data Capture 

Subsystem

Signal Processing 

Subsystem

Reference 

Creation Artefact Detection Rate

Accept/Reject

Artefact

Quality Check

Reject

• Artefact detection 

methods treated as two 

class problem

• Evaluation in literature 

focuses specifically on 

artefact detection module 

only

Live Finger

Non-Artefact Detection Rate

Artefact Detection 

Module

Suspicious Presentation 

Detection Subsystem
Additional Metrics (Artefact Input)

Segmentation

Feature Extraction

Biometric

Capture Sensor

Biometric 

Characteristics

Data Capture 

Subsystem

Signal Processing 

Subsystem

Quality Check

Reference 

Creation

Artefact

Reject

Failure To 

Acquire 

Reject

Artefact Detection Rate

• Artefact detection module will 

contribute to decision to reject

• Other modules (e.g. quality) may 

contribute 

• During testing specific reason for 

rejection may not be known

• Need clarification in 

terminology for system testing 

(this slide) and artefact 

detection module testing (last 

slide)

Live Finger Failure to Enroll 

(Live, Non-Artefact) Artefact Detection 

Module

Suspicious Presentation 

Detection Subsystem

Artefact Detection Rate
What about matching? (Artefact Input)

Segmentation

Feature Extraction

Biometric

Capture Sensor

Biometric 

Characteristics

Data Capture 

Subsystem

Signal Processing 

Subsystem

Quality Check

Reference 

Creation

Reference Reference

Probe

Artefact False Accept

Decision (Reject/Accept)

• Artefact finger may not be 

rejected by earlier 

modules

• If artefact matches stored 

reference, a successful 

artefact attack has 

occurred.

Live Finger

False Reject (Non-artefact)

False Accept (Non-artefact)

Artefact

Data Storage Subsystem

Enrollment Database

Biometric Claim

Artefact Detection 

Module

Suspicious Presentation 

Detection Subsystem

Comparison Subsystem

Comparison

Match?

Decision Subsystem

Comparison Score
On the Relationship between Liveness 

Detection and Challenge-Response
Motivation

Ways to strengthen Authentication Methods

• Increase to multi-factors

– Biometrics 

– Knowledge

– Possession (not addressed further, too application 

specific) 

• Add strength to biometrics with “liveness” (L)

• Add strength to Authentication with ChallengeResponse (CR) schemes
Relationship between L and CR

• Some techniques combine L and CR 

• See illustration in the following table

CR L & CR L 
Primary Examples “L & CR” 

Controlled change 

illuminationPupil size

Multispectral 

illuminationAbsorption

characteristics

Concepts:

ChallengeResponse (based 

on Liveness)

Stimulated intentionally

Primary Examples “L”

Finger perspiration (over 

time)

Hippus (iris) motion/freq

Pulse)

Concepts:

No stimulation (no 

“challenge”)

Passive (receive only) 

Challenge 

Response “Passive”

LIVENESS

(BIOMETRIC CAPTURE 

SUBSYSTEM BASED)

Primary Examples “CR” 

Finger order (random changes by 

system) Correct presentation & 

matching

Digit order Correct 

pronunciation & matching

Security question* Correct 

answer (content) & matching 

* Combination of Knowledge and 

Biometrics

Concepts:

Challenge logic in System 

(server/back-end)

Enrollment of all designed 

variations (multiple fingers, all 

digits 0-9) 

Primary Examples (non-BIO)

Smart ID card (with 

authentication) + PIN

Login name + password + 

randomized security 

question

ID card + scramble pad PIN 

code*

* this example has an added 

cognitive/human/alive aspect 

Concepts:

Involves authentication 

factors other than Biometrics

Challenge can take the form 

of device/card authentication 

(confirm digital cert)

CR-SYSTEM LEVEL

(DOES NOT INVOLVE BIOMETRIC CAPTURE)

CR-BIOMETRIC SYSTEM LEVEL

(INVOLVES SOME ASPECTS EXTERNAL TO THE 

BIOMETRIC CAPTURE SUBSYSTEM)

L and CR relationship (overall)
Summary

• Some Liveness approaches do not involve 

Challenge-Response (L)

• Liveness and Challenge-Response can be use 

together (L&CR)

• Some Challenge-Response approaches involve 

biometrics but not Liveness (CR)

• Some Challenge-Response approaches do not 

involve biometrics (non-BIO) 
Overall Summary

• Categories of Subversive Presentation

– Artificial (Source and Production Methods)

– Human (altered, coerced, non-conformant, conformant, cadaver)

• Suspicious Presentation Detection

– Liveness Detection, Artefact Detection, Altered Finger Detection

• Metrics for measuring performance

– False Suspicious Presentation Detection (FSPD)

• e.g., False Artefact Detection (FAD)

– False Non-Suspicious Presentation Detection (FNSPD)

• e.g., False Non-Artefact Detection (FND)

• Liveness and Challenge Response