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 Liveness Satellite Workshop

International Biometric Performance Conference (IBPC)

March, 2012
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
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
Performance Metrics for the Combination of 

Suspicious Presentation Detection System 

and the Matcher
Artefact Detector and Biometric Matcher

Non-Match

Match

Artefact

Non-Artefact

Matcher Artefact

Detector

Configuration type 1

Artefact

NonX-Artefact
• TAD: True Artefact Detection 

• FAD: False Artefact Detection 

• TND: True Non-Artefact Detection 

• FND: False Non-Artefact Detection 

Matcher

gen

imp FNM

TM

User with 

an 

artefact

(Genuine)

Configuration type 1

Artefact

detector

• FNM: False Non-Match 

• TM: True Match

NonArtefact

Artefact TM-TAD

TM-FND

False accept 

of the artefact

Correct rejection

of the artefact

The combination of Artefact Detection and Matcher

should REJECT the artefact

System may stop

at this point.
• TAD: True Artefact Detection 

• FAD: False Artefact Detection 

• TND: True Non-Artefact Detection 

• FND: False Non-Artefact Detection 

Matcher

gen

imp FNM

TM

User with 

an 

artefact

(Genuine)

Artefact

detector NonArtefact

Artefact FNM-TAD

FNM-FND

Configuration type 1

Artefact

detector

• FNM: False Non-Match 

• TM: True Match

NonArtefact

Artefact TM-TAD

TM-FND

False accept 

of the artefact

Correct rejection

of the artefact

The combination of Artefact Detection and Matcher

should REJECT the artefact
• FNM: False Non-Match 

• TM: True Match

• TAD: True Artefact Detection 

• FAD: False Artefact Detection 

• TND: True Non-Artefact Detection 

• FND: False Non-Artefact Detection 

Matcher

gen

imp

TM

User with 

no 

artefact

(Genuine)

Configuration type 1

Artefact

detector NonArtefact

Artefact TM-FAD

TM-TND

False rejection

of the live person

Correct accept

of the live person

The combination of Artefact Detection and Matcher

should ACCEPT the live person

FNM System may reject

at this point.
• FNM: False Non-Match 

• TM: True Match

• TAD: True Artefact Detection 

• FAD: False Artefact Detection 

• TND: True Non-Artefact Detection 

• FND: False Non-Artefact Detection 

Matcher

gen

imp

TM

User with 

no 

artefact

(Genuine)

FNM Artefact

detector NonArtefact

Artefact FNM-FAD

FNM-TND

Configuration type 1

Artefact

detector NonArtefact

Artefact TM-FAD

TM-TND

False rejection

of the live person

Correct accept

of the live person

The combination of Artefact Detection and Matcher

should ACCEPT the live person
Configuration type 2

Artefact Detector and Biometric Matcher

Non-match

Match Artefact

Non-Artefact

Non-match

Match

0

Artefact

Detector

Matcher

X
Configuration type 2

Artefact

detector

NonArtefact

Artefact TAD

FND

Matcher

gen

imp

User with 

an 

artefact 

(Genuine)

FNM-FND

TM-FND

• FNM: False Non-Match 

• TM: True Match 

• TAD: True Artefact Detection 

• FAD: False Artefact Detection 

• TND: True Non-Artefact Detection 

• FND: False Non-Artefact Detection 

False accept 

of the artefact

Correct rejection

of the artefact

The combination of Artefact Detection and Matcher

should REJECT the artefact

System may stop

at this point.
Configuration type 2

Artefact

detector

NonArtefact

Artefact TAD

FND

Matcher

gen

imp

User with 

an 

artefact 

(Genuine)

FNM-FND

TM-FND

• FNM: False Non-Match 

• TM: True Match 

• TAD: True Artefact Detection 

• FAD: False Artefact Detection 

• TND: True Non-Artefact Detection 

• FND: False Non-Artefact Detection 

gen

imp FNM-TAD

TM-TAD

Matcher

False accept 

of the artefact

Correct rejection

of the artefact

The combination of Artefact Detection and Matcher

should REJECT the artefact
Configuration type 2

Artefact

detector

NonArtefact

Artefact FAD

TND

Matcher

gen

imp

User with 

no 

artefact

(Genuine)

FNM-TND

TM-TND

• FNM: False Non-Match 

• TM: True Match 

• TAD: True Artefact Detection 

• FAD: False Artefact Detection 

• TND: True Non-Artefact Detection 

• FND: False Non-Artefact Detection 

The combination of Artefact Detection and Matcher

should ACCEPT the live person

False rejection

of the live person

Correct accept

of the live person

System may stop

at this point.
Configuration type 2

Artefact

detector

NonArtefact

Artefact FAD

TND

Matcher

gen

imp

User with 

no 

artefact

(Genuine)

FNM-TND

TM-TND

• FNM: False Non-Match 

• TM: True Match 

• TAD: True Artefact Detection 

• FAD: False Artefact Detection 

• TND: True Non-Artefact Detection 

• FND: False Non-Artefact Detection 

gen

imp FNM-FAD

TM-FAD

Matcher

The combination of Artefact Detection and Matcher

should ACCEPT the live person

False rejection

of the live person

Correct accept

of the live person
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
Extra Slides
1 2 3 4

5

Suspicious Presentation Detection 

(SPD) Location

• SPD at biometric 

sensor component 

level

• Based on hardware’s 

intrinsic differentiation 

between real and 

artificial presentation

• No basis for 

evaluation of SPD 

performance

• Independent 

hardware-based SPD

• State of SPD could be 

recorded by system

• Upon successful SPD, 

sample may or may 

not be transmitted to 

signal processing 

subsystem
1 2 3 4

5

Suspicious Presentation Detection 

(SPD) Location

• SPD after sensor 

component level

• Based only on 

captured sample

• In case of successful 

SPD, image may not 

be transmitted to 

signal processing 

subsystem

• State of SPD 

recorded by system
1 2 3 4

5

Suspicious Presentation Detection 

(SPD) Location

• SPD at signal 

processing 

component level

• Based on captured 

sample

• Allows for quality 

control on sample 

before SPD

• In case of successful 

SPD, biometric 

features may not be 

transmitted to 

comparison 

subsystem
1 2 3 4

5

Suspicious Presentation Detection 

(SPD) Location

• SPD after signal 

processing 

component level

• State of SPD 

transmitted with 

biometric features to 

comparison 

subsystem
1 2 3 4

5

Suspicious Presentation Detection 

(SPD) Location

• SPD after comparison 

subsystem

• State of SPD 

transmitted with 

biometric comparison 

score to decision 

subsystem

• Allows for fusion of 

SPD output with 

comparison score