Technology foresight on biometrics for the future of travel - annex II : taxonomy of biometric technologies and biometrics-enabled technological systems.
Date: 2022-10-17
Institutions: Frontex
Subjects: DNA, applied research, biometrics, border control, data collection, foresight, new technology, personal data, research method, research results, technology assessment

Technology ForesighT on BiomeTrics 
For The FuTure oF Travel
annex ii
Taxonomy 
of Biometric 
Technologies and 
Biometrics-Enabled  
Technological 
Systems 

Executive Summary

Research Study

Annex I: Technology  
Foresight Manual

Annex II: Taxonomy 
of Biometric 
Technologies and 
Biometrics-Enabled  
Technological 
Systems

Annex III: 
Patentometric 
and Bibliometric 
Analyses of Biometric 
Technologies 

Technology ForesighT on BiomeTrics 
For The FuTure oF Travel
annex ii
Taxonomy of Biometric 
Technologies and 
Biometrics-Enabled  
Technological Systems 

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Technology ForesighT on BiomeTrics For The FuTure oF TravelTable of Contents 

Figures and Tables #7
Abbreviations and Acronyms #8
Executive Summary #12
Introduction #14

Part 1 – Taxonomy of biometric technologies #18

1	
2	

Methodology #18
Taxonomy description #25
2.1	

Biomolecular biometrics #26
2.1.1	 DNA biometrics #26

2.1.1.1	 DNA phenotyping #26
2.1.1.2	 DNA profiling #27
2.1.1.3	 DNA sequencing #27
2.1.1.4	 Techniques for collecting DNA samples #27

2.1.2	 Other biomolecular biometrics #27

2.1.2.1	 Hand bacteria identification #27

2.2	 Morphological biometrics #28

2.2.1	 Face recognition #28

2.2.1.1	 Thermal infrared face recognition #28
2.2.1.2	 Near-infrared face recognition #29
2.2.1.3	 Video-based face recognition #29
2.2.1.4	 Image-based face recognition #29
2.2.1.5	 3D face recognition #30

2.2.2	 Friction ridge recognition #30

2.2.2.1	 Thermal friction ridge recognition #30
2.2.2.2	 Near-infrared friction ridge recognition #30
2.2.2.3	 3D friction ridge recognition #30
2.2.2.4	 Contactless friction ridge recognition #31
2.2.2.5	 Contact-based friction ridge recognition #31
2.2.2.6	 Fingerprint recognition #31
2.2.2.7	 Palmprint recognition #31
2.2.2.8	 Footprint recognition #31
2.2.2.9	 Finger-knuckle-print recognition #32

2.2.3	 Iris recognition #32

2.2.3.1	 Iris recognition in the NIR spectrum #32
2.2.3.2	 Iris recognition in the visible spectrum #32
2.2.3.3	 Iris recognition at a distance #33

2.2.4	 Vascular pattern recognition #33

2.2.4.1	 Retina recognition #33
2.2.4.2	 Sclera/episclera recognition #33
2.2.4.3	 Finger vein recognition #34
2.2.4.4	 Palm vein recognition #34
2.2.4.5	 Back-of-hand vascular recognition #34
2.2.4.6	 Wrist vein recognition #35

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems2.2.5	 Physiological signal biometrics #35

2.2.5.1	 Heart-rate variability #35
2.2.5.2	 Electrocardiographic signals #35
2.2.5.3	 Phonocardiographic signals #35
2.2.5.4	 Photoplethysmographic signals #36
2.2.5.5	 Encephalographic signals #36
2.2.5.6	 Acoustic properties of the ear canal #36

2.2.6	 Hand geometry recognition #37

2.2.6.1	 Contact-based hand geometry recognition #37
2.2.6.2	 Contactless hand geometry recognition #37

2.2.7	 Other minor morphological biometrics #37

2.2.7.1	 Dental biometrics #37
2.2.7.2	 Tongueprint recognition #38
2.2.7.3	 Ear print recognition #38
2.2.7.4	 Periocular recognition #38
2.2.7.5	 Skin texture recognition #38
2.2.7.6	 Cranial suture scanning #39
2.2.7.7	 Rugoscopy #39
2.2.7.8	 MMW and THz wave body imaging #39

2.3	 Behavioural biometrics #40

2.3.1	 Keystroke recognition #40

2.3.1.1	 Dynamic keystroke recognition #40
2.3.1.2	 Static keystroke recognition #40

2.3.2	 Gait recognition #41

2.3.2.1	 Gait recognition based on video sensors #41
2.3.2.2	 Gait recognition based on radar sensors #41
2.3.2.3	 Gait recognition based on floor sensors #41
2.3.2.4	 Gait recognition based on wearable sensors #41

2.3.3	 Handwriting recognition #42

2.3.3.1	 Dynamic handwriting recognition #42
2.3.3.2	 Static handwriting recognition #42

2.3.4	 Speaker recognition #42

2.3.4.1	 Text-dependent speaker recognition #42
2.3.4.2	 Text-independent speaker recognition #43
2.3.4.3	 Vocal resonance recognition #43
2.3.5	 Other minor behavioural biometrics #43

2.3.5.1	 Complex eye movement patterns (eye-tracking) #43
2.3.5.2	 Oculomotor plant characteristics #43

3	

Technological clusters #44
3.1	 DNA biometrics #47
Infrared face recognition #47
3.2	
2D face recognition in the visible spectrum #47
3.3	
3D face recognition #47
3.4	
Infrared friction ridge recognition #48
3.5	
3D friction ridge recognition #48
3.6	
3.7	 Contactless friction ridge recognition #48
3.8	 Contact-based friction ridge recognition #48
Iris recognition in the NIR spectrum #48
3.9	
Iris recognition in the visible spectrum #49
3.10	
Iris recognition at a distance #49
3.11	

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3.12	 Eye vein recognition #49
3.13	 Hand vein recognition #49
3.14	 Heart signal recognition #49
3.15	 Hand geometry recognition #50
3.16	 Periocular recognition #50
3.17	 Keystroke recognition #50
3.18	 Gait recognition #51
3.19	 Handwriting recognition #51
3.20	 Speaker recognition #51
Conclusions #52

4	

Part 2 – Taxonomy of biometrics-enabled technological systems #53

5	
6	

Methodology #53
Taxonomy description #53
6.1	

Self-service systems #56
6.1.1	 E-gates #56
6.1.2	 Corridors #57
6.1.3	 Kiosks #57
6.1.4	 Remote on-boarding systems #57
Identity document readers and verification sub-systems #58
6.2.1	 Integrated document readers/scanners #58
6.2.2	 Desktop document readers/scanners #58
6.2.3	 Forensic document readers/scanners #58
6.2.4	 Handheld document readers/scanners #59
6.2.5	 Holographic document readers/scanners #59
6.2.6	 Document analysis software #59
Full-body scanning systems #60
6.3.1	 Scanners using mm or THz waves #60
6.3.2	 Scanners using radar wavebands #60
6.3.3	 Systems using quantum sensors #60
6.3.4	 Holographic scanning systems #61
6.3.5	 Camera-based scanning systems #61
Systems based on personal devices #62
6.4.1	 Smartphone device biometrics #62
6.4.2	 Handheld device biometrics #62
6.4.3	 Wearable biometric technologies #62
6.4.4	 Microchip biometric implants #62

6.2	

6.3	

6.4	

6.5	 Movable systems #63

6.5.1	 Mobile (non-autonomous) checkpoints #63
6.5.2	 Semi-autonomous robotic systems #63
6.5.3	 Autonomous robotic systems #63
6.5.4	 Systems for the seamless recognition of persons in transport #63
Large-scale IT systems that deploy biometric comparison components #64
6.6.1	 Traveller information and entry-exit registration systems #64
6.6.2	 Visa and travel authorisation systems #64
6.6.3	 Biometric central databases for border control and law enforce-

6.6	

ment #54

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6.7	 Virtual traveller identification schemes for biometrics-enabled techno-

logical systems #66
6.7.1	 ICAO “Digital Travel Credential” (DTC) #66
6.7.2	 IATA “Travel Pass” #66
6.7.3	 WEF “Known Traveller Digital Identity” (KTDI) #66
6.7.4	 Proprietary schemes #66
6.7.5	 Digital Trust Frameworks #67

7	

Conclusions #68

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Figures and Tables 

Figure 1:  Workflow for constructing the taxonomy of biometric technologies. #22

Figure 2:  Graphic representation of the taxonomy of biometric technologies. #25

Figure 3:  Graphic representation of the taxonomy of biometrics-enabled techno-

logical systems. #55

Table 1: 

Outline of the results of the Patentometric and Bibliometric Analyses of 
20 technological clusters, showing the significance of inventive activity (in 
terms of number of filed patent families, filed patents and scientific papers) 
and the inferred stage in the technology lifecycle. The clusters are sorted 
in descending order according to the number of patent families. The num-
ber in the left-hand column is the ordinal number associated with each of 
the clusters throughout the study. #24

Table 2: 

List of 20 technological clusters and corresponding biometric technolo-
gies belonging to the third level of the taxonomy, which were considered 
for conducting Patentometric and Bibliometric Analyses and used in the 
various phases of the Tech Foresight on Biometrics. #45

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTemsAbbreviations and Acronyms

Abbreviation / 
Acronym

Definition

2D

3D

ABC

API

BCP

CBP

DNA 

Two-dimensional

Three-dimensional

Automated Border Control

Advance Passenger Information

Information concerning the passengers whom carriers will transport to an authorised border cross-
ing point, through which these persons will enter the territory of a Schengen Member State, which 
carriers are obliged to transmit, by end of check-in, at the request of the authorities responsible for 
carrying out checks on persons at external borders

Derived from Art. 3 of Council Directive 2004/82/EC (Directive on the obligation of carriers to com-
municate passenger data)

Border Crossing Point, as defined in point 8 of Article 2 of Regulation (EU) 2016/399 (Schengen 
Borders Code)

US Customs and Borders Protection

Deoxyribonucleic acid

DOCDB 

DOCument DataBase

DOCDB is the European Patent Office’s master documentation database with worldwide coverage. 
It contains bibliographic data, abstracts, citations and the DOCDB simple patent family, but no full 
text or images.

The DOCDB simple patent family is defined by the European Patent Office as “a collection of patent 
documents that are considered to cover a single invention. The technical content covered by the appli-
cations is considered to be identical” (https://www.epo.org/searching-for-patents/helpful-resources/
first-time-here/patent-families/docdb.html)

DTC

ICAO Digital Travel Credential

Travel Credentials issued in a digital format, intended to temporarily or permanently substitute a 
conventional passport with a digital representation of the traveller’s identity, which can in turn be 
validated using the travel document issuing authority’s public key infrastructure (see ICAO publica-
tion “Guiding Core Principles for the Development of Digital Travel Credential”)

EC

ECG 

European Commission

Electrocardiographic

ECRIS-TCN

European Criminal Records Information System – Third Country Nationals. 

Currently under development, ECRIS-TCN will be a centralised system that allows Member State 
authorities to identify which other Member State(s) hold criminal records on third country nation-
als or stateless persons

Established by Regulation (EU) 2019/816

EEA

EEG

EES

European Economic Area

Electroencephalographic

Entry-Exit System

Currently under development, the EES will electronically register the time and place of the entry and 
exit from the EU of third country nationals as well as calculate the duration of their authorised stay. 
It will replace the obligation to stamp the passports of third country nationals.

Established by Regulation (EU) 2017/2226

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Technology ForesighT on BiomeTrics For The FuTure oF TravelAbbreviation / 
Acronym

Definition

EIS

eMRTD

EPO

ESTA

ETA

ETIAS

EU

eu-LISA

Europol Information System

Electronic Machine Readable Travel Document

European Patent Office

US Electronic System for Travel Authorisation

Canadian Electronic Travel Authorisation

European Travel Information and Authorisation System

Currently under development, the ETIAS will be a pre-travel authorisation system for visa-exempt 
travellers. Its key function is to verify whether a third country national meets entry requirements 
before travelling to the Schengen Area

Established by Regulations (EU) 2018/1240 and (EU) 2018/1241

European Union

European Union Agency for the Operational Management of Large-Scale IT Systems in the Area of 
Freedom, Security and Justice

Governed by Regulation (EU) 2018/1726

Eurodac

European Asylum Dactyloscopy Database

European database that collects and processes the digitised fingerprints of asylum seekers and irregu-
lar migrants. It helps determine which Member State is responsible for examining asylum applications

Established by Regulation (EU) No 603/2013

Europol

European Union Agency for Law Enforcement Cooperation

Governed by Regulation (EU) 2016/794

Finger-Knuckle-Print

The European Border and Coast Guard Agency

Governed by Regulation (EU) 2019/1896

Frontex staff who participated in the Tech Foresight on Biometrics

FKP

Frontex

Frontex 
Representatives

GHz

Gigahertz

Group of 
Experts

Subject-matter experts representative of stakeholders selected to participate in the consultation and 
collective intelligence activities conducted within the framework of the Tech Foresight on Biometrics, 
such as the workshops and the Delphi Survey. This also included members of the Research Team 
and Frontex Representatives

HRV

IATA

ICAO

ID

IEC

IR

ISO

IT

KTDI

KYC

LWIR

Heart-Rate Variability

International Air Transport Association

International Civil Aviation Organization

Identity Document

International Electrotechnical Commission

Infrared

International Organization for Standardization

Information Technology

Known Traveller Digital Identity

Know Your Customer

Long-Wavelength Infrared; electromagnetic radiation with 8-15 µm wavelength

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTemsAbbreviation / 
Acronym

Definition

MBC

MRTD

MMW

MR

MV

MWIR

NEXTT

NIR

NLP

NZeTA

OPC

Manual Border Control

Machine Readable Travel Document

Millimetre Waves; electromagnetic radiation with 1-10 mm wavelength

Motion Recording

Machine Vision

Medium-Wavelength Infrared; electromagnetic radiation with 3-8 µm wavelength

New Experience Travel Technologies (https://www.nextt.aero/en/)

Near-Infrared; electromagnetic radiation with 0.78-1.0 µm wavelength

Natural Language Processing

New Zealand Electronic Travel Authority

Oculomotor Plant Characteristics

OpenAIRE

Open Access Infrastructure for Research in Europe (https://www.openaire.eu/)

PCG 

PDA

PII

PIP

PNR

Phonocardiography

Personal Digital Assistant

Personally Identifiable Information. Any information related to an identifiable person

Proximal Interphalangeal

Passenger Name Record

PNR data are unverified information provided by passengers and collected by air carriers to enable 
the reservation and check-in processes

Regulated by Directive (EU) 2016/681

PPG

Photoplethysmography

Research Team Employees of the of the Contractor selected by Frontex to conduct the Technology Foresight on 
Biometrics for the Future of Travel (Steinbeis 2i GmbH) and its Subcontractors (4CF Sp. z o.o., Erre 
Quadro S.r.l. and the Instytut Optoelektroniki – Wojskowa Akademia Techniczna), who jointly pro-
vided expertise in the following fields:

•  Project Management

•  Strategic Technology Foresight 

•  Biometrics for border control systems

•  Smart and Autonomous Systems

•  Systems Engineering for border control

Research Team 
Experts

Members of the Research Team who provided subject-matter expertise in the following disciplines:

•  Biometrics for border control systems

•  Smart and Autonomous Systems

•  Systems Engineering for border control

Schengen 
Borders Code

Regulation (EC) No 562/2006 of the European Parliament and of the Council of 15 March 2006 
establishing a Community Code on the rules governing the movement of persons across borders

SIS

Schengen Information System

Supports the exchange of information on persons and objects between the national police, border 
control, customs, visa and judicial authorities. It acts as a compensatory measure for the removal of 
border checks in the Schengen Area

Established by Regulations (EU) 2018/1860, 2018/1861 and 2018/1862

SMW

Sub-Millimetre Waves; electromagnetic radiation with 1 mm – 0.01 mm wavelength

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Technology ForesighT on BiomeTrics For The FuTure oF Travel 
Abbreviation / 
Acronym

Definition

SSS

SWIR

Tech Foresight 
on Biometrics

Self-Service System

Short-Wavelength Infrared; electromagnetic radiation with 1-3 µm wavelength

Technology Foresight on Biometrics for the Future of Travel

TF

THz

µm

UV

VIS

Technology Foresight

Terahertz

Terahertz waves are electromagnetic radiation with 1 mm – 0.01 mm wavelength

Micrometre

Ultraviolet

Visa Information System

VIS supports the implementation of the EU’s common visa policy and facilitates border checks. The 
system enables dedicated national authorities to enter and consult data, including biometrics, for 
short-stay visas to the Schengen Area

Governed by Regulation (EC) No 767/2008

WEF

WTTC

WS

World Economic Forum

World Travel and Tourism Council

Wearable Sensor

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTemsExecutive Summary

Biometrics is a broad and diverse scientific field, where physics, engineering, medicine, 
psychology and more come into contact. Having to deal with such a level of hetero-
geneity, Frontex has highlighted the importance of categorising the main biometric 
technologies with potential to find applications in the operational fields of interest for 
the EBCG community. 

Starting from Frontex’s requirements, the Research Team designed two different tax-
onomies, mapping respectively biometric technologies (e.g. Fingerprint recognition 
and Face recognition) and biometrics-enabled technological systems (e.g. e-gates and 
autonomous robotic systems). To deal with the breadth and complexity of the biomet-
rics domain, as well as to focus on biometric technologies per se, the two taxonomies 
were designed by following two distinct approaches, featuring different levels of thor-
oughness and complexity, which are discussed in the two parts of this report.

Together, the two taxonomies represent one of the results of the State-of-the-Art Review 
step in the Tech Foresight on Biometrics, and an essential input to the subsequent phases 
of the adopted Technology Foresight methodology. The taxonomy of biometric tech-
nologies, in particular, was used to extract a set of technological clusters on which Pat-
entometric and Bibliometric Analyses were conducted and which represented the input 
to the successive steps in the Filtering the Results, Deep Analysis and Mapping Capabil-
ities phases of the Technology Foresight methodology.

After describing the methodologies we followed, this document reports the results of a 
systematic categorisation analysis conducted on the set of identified biometric technol-
ogies and biometrics-enabled technological systems, for which the scope was limited 
to applications in border checks, biometric recognition or access control. Technologies 
and systems finding exclusive application in border surveillance were considered out-
side the scope of this work, as were solutions aimed at using biometrics for emotion 
and behaviour detection.

The taxonomy of biometric technologies was developed through an iterative process 
that started from a list of research areas in biometrics, identified ab initio by the Research 
Team. The process then relied on the analysis of the patents gathered from a propri-
etary database (derived from the EPO database) and the scientific literature collected 
from OpenAIRE for the scanning of state-of-the-art documentation, the extraction of 
suitable terminology and the description of technologies. Such analysis was performed 
through NLP-based automatic tools and the strong involvement of the Research Team 
Experts. Transverse knowledge is, in fact, necessary to design proper and comprehen-
sive taxonomies. Patents, aiming at describing and protecting inventions, represent a 
valuable and accessible source of information from which both known and novel tech-
nologies, techniques and/or methodologies may be retrieved. However, due to their 
ontological characteristics, patent databases may not contain sufficient information 
for developing comprehensive taxonomies. Therefore, to lower the risks arising from 
patents’ limitations, scientific publications and Research Team Experts’ knowledge also 
had to be considered. This approach led to the creation of a three-level taxonomy tree 
formed by three biometric technological macro-areas, 14 biometric technological fields 

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and 57 specific biometric technologies and modalities. A definition is provided in this 
document for every node and leaf of the tree.

The taxonomy of biometrics-enabled technological systems was created starting 
from a set of technological solutions of potential interest for the research study. Such 
a set was first expanded and then consolidated and validated by exploiting the knowl-
edge and expertise of the Research Team Experts. The outcome of such an approach is 
represented by a two-level taxonomy tree formed by seven macro-areas and 31 fam-
ilies of technological systems. A definition was provided in this document for every 
node and leaf of the tree.

In conclusion, through the present work, the Research Team was able to logically map 
the biometrics realm from the point of view of the basic technologies and technolog-
ical systems, thus fulfilling the objective of the research study to establish a common 
understanding of the biometrics domain and create a reference document that could 
be used in research and innovation activities. The application of NLP methodologies 
for the analysis of patents and scientific papers was confirmed as a useful practice for 
intercepting uncommon or niche technologies (e.g. rugoscopy and hand bacteria iden-
tification), which were included in the taxonomy of biometric technologies together 
with the most known ones (e.g. face recognition and iris recognition). It is worth point-
ing out that due to the need to limit the complexity of the taxonomy, and in general 
of the overall Research Study, to a manageable level, this work did not consider the 
mainstream biometrics-enabling and/or cross-cutting scientific and technological fields, 
such as: artificial intelligence in biometric systems; privacy-preserving and privacy-en-
hancement solutions for the security of identity; techniques for biometric database con-
struction; document verification and fraud detection; manipulation attack detection and 
protection against attempts/techniques to falsify biometrics. However, a brief overview 
of these topics can be found in the Research Study. 

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems  
	
Introduction 

This document reports the results of a categorisation analysis conducted on a set of 
biometric technologies and biometrics-enabled technological systems relevant to bor-
der checks, whose outcome is represented by: 
• 
• 

a taxonomy of biometric technologies, covered in Part 1,
a taxonomy of biometrics-enabled technological systems, covered in Part 2.

The term taxonomy is a compound word formed by the Greek terms τάξις (taxis, mean-
ing ‘order’ or ‘arrangement’) and νόμος (nomos, meaning ‘law’ or ‘science’) and may 
be defined as the practice and science of categorisation or classification.1 The classi-
fied units forming a taxonomy are called taxa. A taxonomy is a scheme that is gener-
ally organised in a hierarchical order of subtypes and supertypes. A popular example of 
taxonomy is the one developed by Sneath and Sokal that establishes a classification of 
organisms based on their similarities.2 Taxonomies are generally presented as hierar-
chies where each level is a subset of the level above it. For example, a biology taxon-
omy would probably include concepts such as mammals, as a subset of animals, and 
dogs or cats, representing subsets of mammals.3 

As explained in Annex I – Technology Foresight Manual, within the Technology Fore-
sight methodology the State-of-the-Art Review is executed in two consecutive steps: 
1. 
2. 

Identification of the main areas of research; 
Development of a technological taxonomy.

The results of Step 1 are reported in Section 3.1 of the Research Study and represented 
the starting point of the analysis as explained in Chapter 1 of this report.

The work was performed in accordance with the requirements expressed in the Terms 
of Reference4 for the Research Study, Section 3.1 – Objectives of the assignment, namely:
• 
“Comprehensive Technology Taxonomy: Develop a thorough, extensive and detailed 
technology taxonomy for future biometrics and biometrics-enabled technological 
systems. A technology taxonomy is understood as the mapping of all the relat-
ed research topics, sub-topics, and technologies potential to find applications in 
operational fields of interest for the EBCG community in a categorical and logical 
manner. The goal is to establish a common understanding and create a reference 
document, which will be used by Frontex in research and innovation activities”.

1 

2 

3 

4 

M. Schatten, M. Baca and K. Rabuzin, “A Taxonomy of Biometric Methods”, ITI 2008 – 30th Inter-
national Conference on Information Technology Interfaces, 2008.

P. H. A. Sneath, R. R. Sokal and W. H. Freeman, “Numerical Taxonomy. The Principles and Prac-
tice of Numerical Classification”, Systematic Zoology, no. 24, pp. 263-268, 1975.

R. J. Brachman, “What IS-A Is and Isn't: An Analysis of Taxonomic Links in Semantic Networks”, 
Computer, vol. 16, no. 10, pp. 30-36, 1983.

See https://etendering.ted.europa.eu/cft/cft-documents.html?cftId=6982

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Considering the breadth and technological complexity of the biometrics domain, and 
the broad and transversal knowledge needed to create comprehensive taxonomies, 
two approaches were followed to construct the two taxonomies:
• 

The methodology used to develop the taxonomy of biometric technologies 
focused on the analysis of technical and scientific documentation, which were 
assumed to contain all necessary knowledge. More specifically, patents and sci-
entific papers were considered. Given the significant volume of literature on bio-
metric technologies, automatic tools5 were used to (a) analyse the textual content 
of patents and scientific papers, respectively retrieved from the patent database6 
and the OpenAIRE7 database and (b) collect suitable terminology for creating the 
taxonomy. The Research Team Experts were tasked with amending and validat-
ing the information gathered.
The taxonomy of biometrics-enabled technological systems was developed pri-
marily through desk research conducted by the Research Team Experts.

• 

Moreover, the general structure and the content of both taxonomies were devised to 
allow a common understanding of the biometrics domain and to make sure they could 
be used by Frontex as reference documents in its research and innovation initiatives. 

In addition to the knowledge requirements, a good definition of the scope and boundar-
ies of the work is essential for a well-designed taxonomy construction process, because 
it helps the analysts to focus on the target. Therefore, the scope of the categorisation 
analysis was limited to those biometric technologies and biometrics-enabled techno-
logical systems which could find application in border checks, biometric recognition, 
or access control. Additional constraints were imposed by not considering border 
surveillance or emotion and behaviour detection within the scope of the taxonomy.8 
Consequently, while constructing the taxonomy of biometric technologies, a specific 
technology was considered within the scope of interest if and only if at least one refer-
ence to its application in at least one field from border checks, access control, or bio-
metric recognition was found during the automated analysis of patents and/or papers. 
A biometric technology was also taken into consideration if a single ‘in-scope’ applica-
tion was known to the Research Team Experts.

Furthermore, due to the need to limit the complexity of the taxonomy, and of the over-
all Research Study, to a manageable level, the mainstream biometrics-enabling and/or 
cross-cutting scientific and technological fields were not considered within the scope of 
the taxonomical categorisation. These include artificial intelligence in biometric systems; 
privacy-preserving and privacy-enhancement solutions for the security of identity; tech-

5 

6 

7 

8 

Erre Quadro’s proprietary tools, the Domain Terminology Extractor, Smart Ranker and Weight-
ed Clusterer. For more information, see Annex I – Technology Foresight Manual.

Erre Quadro’s proprietary patent database based on the Patstat Service provided by the Euro-
pean Patent Office, see Chapter 1.

OpenAIRE (Open Access Infrastructure for Research in Europe 2020, https://www.openaire.eu/)) 
is a European platform whose mission is to provide unlimited, barrier free and open access to 
research outputs financed by public funding in Europe.

Although not considered within the scope of the taxonomy, behaviour detection was in gen-
eral considered within the scope of the research study, but limited to solutions aimed at the 
detection of people in need of assistance or special care (e.g. not moving for a long period of 
time, disoriented children or elderly people).

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niques for biometric database construction; document verification and fraud detection; 
manipulation attack detection and protection against attempts/techniques to falsify bio-
metrics. However, a brief overview of these topics can be found in the Research Study.

The retrieved set of biometric technologies and biometrics-enabled technological sys-
tems were categorised and included in two different taxonomy trees, made up respec-
tively of:
• 

the first level, consisting of three biometrics macro-areas; 
the second level, including 14 technological fields; 
the third level, comprising 57 biometric technologies and modalities.

a three-level structure composed of: 
• 
• 
• 
a two-level structure composed of: 
• 
• 

• 

the first level, consisting of a set of seven groups; 
the second level, including 31 categories.

Once created, the taxonomy of biometric technologies was used to identify a set of 
20 biometric technological clusters (see Chapter 3) on which Patentometric and Biblio-
metric Analyses9 were conducted as part of the Insight Hunt phase of the Technology 
Foresight methodology.10 Since not all the identified technological clusters have the same 
potential relevance for border check operations, a multi-level assessment was performed 
with the support of the Group of Experts through a Delphi Survey. This consultation was 
part of the Filtering the Results phase of the Technology Foresight methodology and 
allowed us to shortlist the five most promising clusters suitable for the Deep Analysis 
and the Mapping Capabilities phases, where technological roadmaps were finally orig-
inated for those clusters and capability readiness analyses were conducted on them. 

The taxonomy of biometrics-enabled technological systems played an essential role in 
guiding the development of roadmaps,11 where a specific layer was included to envis-
age future products and systems that might emerge exploiting the selected techno-
logical clusters.

This report is divided into two parts:
• 

Part	1	–	Taxonomy	of	biometric	technologies. This part of the document is 
structured in four chapters as follows:
• 
Chapter 1 – Methodology 
• 
Chapter 2 – Taxonomy description, organised in sections and paragraphs 
that follow the same hierarchical structure of the taxonomy
Chapter 3 – Technological clusters 
Chapter 4 – Conclusions includes a set of final considerations about the 
outcomes of the taxonomical categorisation

• 
• 

Part	2	–	Taxonomy	of	biometrics-enabled	technological	systems. This part of 
the document is structured in three chapters as follows:
• 

Chapter 5 – Methodology

See Annex III – Patentometric and Bibliometric Analyses of Biometric Technologies.

See Annex I – Technology Foresight Manual, Sections 2.1 and 2.2.

See the Research Study, Chapter 9.

• 

9 

10 

11 

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• 

• 

Chapter 6 – Taxonomy description, organised in sections and paragraphs 
that follow the same hierarchical structure of the taxonomy
Chapter 7 – Conclusions provides a set of final considerations about the 
outcomes of the taxonomical categorisation

Should the reader need a description of basic concepts regarding biometrics, EU bor-
der management or patents, or clarifications concerning the meaning of some of the 
terms used within this document, the Research Study contains, in Appendix 6, a Glos-
sary of basic terms and definitions that can be of support in this regard.

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Part 1 – Taxonomy of 
biometric technologies

1. 

Methodology

This chapter describes in detail the methodology devised by the Research Team to con-
struct the taxonomy through a systematic analysis and categorisation process on the 
basis of datasets of patents and scientific papers. 

The taxonomy of biometric technologies was iteratively created starting from a pre-
liminary list of areas of research potentially relevant to the field of biometrics for bor-
der checks, originated, in the initial phases of the Tech Foresight on Biometrics, by the 
Research Team Experts. In total, 46 main areas of research were identified and prelimi-
narily classified into three categories: applications, biometric technologies and biomet-
rics-enabling technologies (hereinafter also referred to as “transversal scientific fields” or 
“cross-cutting technologies”). The complete set of results regarding the identification of 
the areas of research is reported in the Research Study, Section 3.1.

The taxonomy development process relied on a systematic analysis of patents and sci-
entific papers for the retrieval of suitable terminology and descriptions of technolo-
gies. Patents, represent a powerful and freely accessible source of information where 
field-specific technologies, techniques and/or methodologies can be found. Patents 
represent a privileged source of technical information for several reasons:
• 
• 

80% of the technical information contained in patents is not available elsewhere.12, 13
Patents precede market launch as well as scientific publications since the latter 
may compromise the patentability of inventions.
Patents entail significant costs for companies, and thus are only filed to protect 
innovations considered crucial to an enterprise, i.e. innovations worthy of invest-
ments likely to be put into production. Thus, the correlation between patents and 
the economic impact of the innovations they describe is much more direct than 
that of research papers.
Patents describe innovative and emerging technologies that are often character-
ised by a higher level of maturity and feasibility than those presented in papers.
Finally, patent data are publicly available and are easy to find for any sector.

• 

• 

• 

Scientific publications may also describe cutting edge technologies, but:
• 

Usually, in high-tech sectors, fewer technologies are discussed in papers than 
in patents.

12 

13 

M. W. Kütt, and M. Schmiemann, “Quick Scan: a novelty search service in the framework of 
Euro-R&D programmes”, World Patent Information, vol. 20, no. 2, pp. 146-147, 1998.

P.J. Terragno, “Patents as Technical Literature”, IEEE Transaction on Professional Communication, 
vol. 22, no. 2, pp. 101-104, 1979.

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• 

• 

• 

• 

• 

Technologies that are reported in papers may be less feasible and advantageous 
from an industrial manufacturing point of view, or may never enter the market 
at all.
Although relevant, some technical information might be considered unsuitable 
for publication.
The number of papers on a specific technology does not necessarily correlate 
with its relevance for the economy.
Freely available information in databases of non-open-access scientific journals 
is normally limited to titles and abstracts of papers, and the cost for access to a 
comprehensive database can be very high.
The available search engines often have severe limitations (e.g. in the Scopus14 
database, the search field is limited to 250 characters; no advanced search func-
tions are available and the user cannot download more than 100 articles at a time).

A proprietary patents database,15 based on the Patstat Service16 provided by the Euro-
pean Patent Office and containing more than 120 million patents, was used as the data 
source for patent analysis. However, it must be pointed out that patent databases do 
not contain an exhaustive representation of all existing technologies. Many inventions 
and innovations are, in fact, not patented. One reason is the strict criteria that inven-
tions must satisfy to be granted a patent, whose interpretation differs over time and 
across countries, resulting in long and delicate application processes. Furthermore, 
patenting is not the only possible protection mechanism for innovation. Other import-
ant and powerful protection mechanisms include: trade secrets (whose related inven-
tions and innovations could not be discovered using any other source of information); 
speedy product development; the complexity of product design; control of distinguish-
ing capabilities. This means that not all technologies and innovations will find a place 
within the patent universe. Furthermore, due to the duration and mechanisms of the 
application process, patent databases do not exhaustively cover the 18 months17 pre-
ceding the searches (the so-called blind period, during which applications are secret).

That being said, the ontological characteristics and the structure of patents (where both 
the content and the inner organisation of documents are standardised in many ways) 
allow a reliable and scalable NLP approach to the analysis. Furthermore, even if a par-
ticular methodology or technology is not the central object of any invention, thus not 
being patented in any way, it could still be cited (and therefore intercepted) or referred 
to within the description of other inventions. This way, the risk of failure in intercepting 
specific technologies or techniques is low.

However, to lower the risks arising from patents’ characteristics, scientific publications 
extracted from the OpenAIRE database were also considered. 

14 

15 

16 

17 

https://www.scopus.com/

Owned by Erre Quadro, described in more detail in Annex III – Patentometric and Bibliometric 
Analyses of Biometric Technologies – Section 3.1.

https://www.epo.org/searching-for-patents/business/patstat.html

It must be highlighted that the Erre Quadro patent database used for the present work was 
updated to the first quarter of 2021. Thus, the part of the database concerning the period after 
the first half of 2019 is obviously incomplete.

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In the light of the above, to build the taxonomy of biometric technologies, an iterative 
process was followed and fine-tuned, including the use of specialised software tools 
as well as frequent collaboration and interaction with both the Research Team Experts 
and Frontex, thus allowing a continuous alignment on the objectives and the outcomes. 

The workflow is shown in Figure 1 and comprises the following steps:
1. 

2. 

3. 

4. 

5. 

6. 

Research areas defined. The identification of the main areas of research in the 
Technology Foresight process allowed the creation of the preliminary knowledge 
and terminology used to query databases of patents and papers.
Terminology construction. In this step, the terminology for querying the patents 
and papers databases was created based on a set of keywords. In the first iter-
ation of the process, the preliminary list of research areas compiled in Step 1 
was used to extract the first list of biometric technologies.18 These were directly 
inputted to Step 6, Technology validation, to obtain the first set of keywords for 
designing search queries. In subsequent iterations, the set of keywords for ter-
minology construction was the one created in Step 6.
Extraction of publications and patents from databases. Sets of patents and papers 
were retrieved from the databases through queries which used the terminology 
created in the previous step. 
Extraction of technologies. The datasets of patents and papers obtained in the 
previous step were filtered and analysed using the Domain Terminology Extractor 
tool19 to expand and validate the list of keywords used in Step 2, as well as to 
extract terms that could be used to describe biometric technologies, techniques 
and modalities. Technical literature (especially patents) is often full of synonyms 
and/or multiple ways to describe a single concept.
Desk research. Concurrently to the automatic extraction of patent and paper sets, 
brief desk research in industrial literature, online sources and scientific publications 
was also performed to look for additional terminology and omitted biometric 
technologies. This step was also conducted to validate possible unfamiliar terms 
extracted from patents and papers as well as to verify the possible relationships 
between the technologies encountered. Such assessments were necessary for 
the initial construction of the taxonomy.
Validation of technologies. Whenever an additional biometric technology was 
extracted in Steps 4 and 5, a validation step occurred, consisting of searching in 
patents or scientific literature for at least one application falling within the scope 
of the research study. More specifically, a biometric technology or modality was 
validated if, and only if, at least a relevant application in at least one of the relevant 
fields (biometric recognition, border checks and/or access control) was described 
in the retrieved documents. Feedback from the Research Team Experts was also 
used to corroborate the results and to support drafting the technical descrip-
tions of the validated technologies. Finally, the outcomes of this step consisted in:
a) 
validated biometric technologies suitable for inclusion in the taxonomy 
(input to Step 7); 

18 

19 

As mentioned in the Introduction, the work described in Part 1 focused on biometric technol-
ogies only.

Proprietary tool of Erre Quadro. For more information on its functionalities, see Annex I – Tech-
nology Foresight Manual – Chapter 19

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7. 

8. 

9. 

10. 

b) 

a set of keywords for the identification of other relevant biometric technol-
ogies, fed-back to Step 2 to extend and refine the search queries.
Preliminary taxonomy creation. Once a significant number of technologies were 
retrieved and validated, a preliminary taxonomy was created. The previous steps 
(starting from Step 2) were then iterated. This highly iterative process was per-
formed to ensure that no relevant in-scope biometric technologies were miss-
ing from the taxonomy. However, it must be highlighted that because biometrics 
is a broad, complex and expanding domain, it cannot be ruled out that minor, 
unusual or niche technologies might still have eluded the Research Team Experts’ 
and the automatic tools’ grasp.
Taxonomy consolidation. Once no additional value was created by reiterating 
Steps 2-7 (meaning that no additional technologies, relationships between the 
technologies or keywords were found), the preliminary taxonomy was consoli-
dated within this step.
Taxonomy validation. The taxonomy was reviewed and validated in close collab-
oration with Frontex before releasing the final version.
Taxonomy of biometric technologies. The taxonomy was organised into three 
hierarchical levels: 
• 

The first level, consisting of three macro-areas: biomolecular biometrics, 
morphological biometrics and behavioural biometrics;
The second level, including 14 technological fields belonging to the three 
macro-areas;
The third level, comprising all 57 specific biometric recognition or acqui-
sition technologies and modalities (five biomolecular, 39 morphological 
and 13 behavioural), each representing a narrow family of similar applica-
tions within a technological field. 

• 

• 

A graphical representation of the taxonomy of biometric technologies is report-
ed in Figure 2, while a brief description of all the components of the taxonomy 
(macro-areas, technological fields and specific technologies/modalities) can be 
found in the next section.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems 
workflow

feedback

Step in the taxonomy 
construction process

Task conducted in 
another step of the 
Technology Foresight 
process

Software tool

Feedback from 
Research Team 
Experts

Identification of the  
main areas of research  
in biometrics

1. Research areas defined

Patents database  
and OpenAIRE scientific 
publications database

Domain Terminology 
Extractor

Feedback from 
Research Team 
Experts

2. Terminology construction

3. Extraction of publications 
and patents from databases

4. Extraction of technologies

5. Desk research

6. Validation of technologies

7. Preliminary taxonomy 
creation

8. Taxonomy consolidation

Feedback from 
Frontex

9. Taxonomy validation

10. Taxonomy of 
biometric technologies

Clustering 
of biometric 
technologies

Patentometric and bibliometric 
analyses of biometric technologies 
and analysis of EU-funded projects

Weighted Clusterer 
Domain Terminology Extractor  
Smart Ranker

Figure 1: Workflow for constructing the taxonomy of biometric technologies. 

The workflow in Figure 1 shows that the creation of the taxonomy is followed by two 
subsequent tasks in the Technology Foresight process: Clustering of biometric technol-
ogies and Patentometric and Bibliometric Analyses of Biometric Technologies.

Within the task of Clustering of biometric technologies, the third-level technologies were 
grouped into a set of 20 technological clusters, each representing a level of abstraction 
defined in order to enable Patentometric and Bibliometric Analyses as well as to assure 

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the usability of the taxonomy in the successive phases of the Technology Foresight on 
Biometrics (as briefly described in the Introduction). Preliminary identification of the 
clusters was made by using the Weighted Clusterer tool,20 which thanks to an ML algo-
rithm is able to interpret the content of document sets supporting tasks where docu-
ments, as well as technologies or technological applications, have to be categorised.21 
The final identification of the clusters (described in detail in Chapter 3) was based on the 
numerosity of the associated documental sets (patents and scientific publications), 
the technological affinity between taxonomical third-level technologies and the rele-
vance in target applications (border checks, biometric recognition, and access control). 

Within the following task of Patentometric and Bibliometric Analyses of Biometric Tech-
nologies, those analyses were conducted on the 20 clusters.22 Fourteen of the 57 bio-
metric technologies composing the third level of the taxonomy were neither included 
in the 20 technological clusters, nor considered when performing Patentometric and 
Bibliometric Analyses. This was because either the datasets of patents and scientific pub-
lications for this group of technologies were too narrow for quantitative analyses, or 
the technologies per se were not sufficiently relevant to the domain of border checks.

One of the most significant outcomes of this task is Table 1,23 where for each cluster 
the significance of inventive activity (in terms of number of filed patent families,24, filed 
patents and published scientific papers) is shown, along with the stage in technology 
lifecycle inferred based on Altshuller’s Theory of Inventive Problem Solving.25

• 

Some succinct considerations are reported here:
•  More than half (13) of the clusters were assessed to be in their maturity stage. 
Maturity does not imply a rapid decline, since inventive activity may still occur, 
but usually with a lower rate than in past years. These are technologies that 
are	ready	to	be	used.  
Five clusters were assessed as growing fields, where performances and profit-
ability26 are presumably increasing relatively quickly. 
Two clusters were assessed as being in the childhood stage of their lifecycle. 
These are fields that are expected to grow in the future.
Three clusters were assessed as being of minor relevance within the scope of the 
research study.

• 

• 

20 

21 

22 

23 

24 

25 

26 

Proprietary tool of Erre Quadro. For more information on the functionalities of the Weighted 
Clusterer see Annex I – Technology Foresight Manual – Chapter 21.

For more details on how this tool was used to analyse technological clusters, please refer to 
Annex III – Patentometric and Bibliometric Analyses of Biometric Technologies.

Annex III – Patentometric and Bibliometric Analyses of Biometric Technologies contains the 
entire set of results achieved by the execution of this task.  

This table is an extract of Table 84 in Annex III – Patentometric and Bibliometric Analyses of 
Biometric Technologies – Section 5.21.

Patent families are intended as DOCDB patent families as defined in Abbreviations and Acro-
nyms. 

G. Altshuller and A. Williams, “Creativity as an exact science”, New York: Gordon and Breach 
Science Publishers, 1984.

See Annex III – Patentometric and Bibliometric Analyses of Biometric Technologies – Section 
2.2, for a definition of these terms.

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Table 1: Outline of the results of the Patentometric and Bibliometric Analyses of 20 technological clusters, 
showing the significance of inventive activity (in terms of number of filed patent families, filed patents and 
scientific papers) and the inferred stage in the technology lifecycle. The clusters are sorted in descending 
order according to the number of patent families. The number in the left-hand column is the ordinal num-
ber associated with each of the clusters throughout the study.  

#

Technological clusters

Patent 
families

Patents

Papers

Stage in technology lifecycle

20

Speaker recognition

1,760

7,468

848

Maturity

8

3

19

12

2

7

9

4

13

1

15

17

14

11

10

5

6

18

16

Contact-based friction ridge 
recognition

2D face recognition in the visible 
spectrum

1,758

8,599

158

Maturity

1,437

5,012

580

Maturity

Handwriting recognition

Eye vein recognition

Infrared face recognition

Contactless friction ridge 
recognition

Iris recognition in the NIR 
spectrum

3D face recognition

Hand vein recognition

DNA biometrics

Hand geometry recognition

Keystroke recognition

Heart signal recognition

Iris recognition at a distance

Iris recognition in the visible 
spectrum

Infrared friction ridge recognition

3D friction ridge recognition

Gait recognition

Periocular recognition

1,273

1,048

1,047

6,347

5,117

5,111

811

3,974

650

561

532

473

428

378

267

259

222

195

120

32

27

3,068

2,545

1,958

2,556

2,349

1,482

1,207

1,285

1,212

843

571

163

197

821

544

151

25

30

269

457

168

186

129

134

77

40

66

41

67

38

Maturity

Maturity

Maturity

Maturity

Maturity

Maturity

Maturity 

Maturity – minor relevance

Maturity – minor relevance

Maturity – minor relevance

Growth

Growth

Growth

Growth

Growth

Childhood

Childhood

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Taxonomy description

The final tree-style representation of the proposed taxonomy of biometric technolo-
gies is shown in Figure 2. The following sections report a description for each element 
of the tree.

Figure 2: Graphic representation of the taxonomy of biometric technologies.

Biomolecular  
Biometrics

1.1.    DNA biometrics

1.1.1.   DNA phenotyping
1.1.2.   DNA profiling
1.1.3.   DNA sequencing
1.1.4.   Techniques for collecting DNA samples

1.2.    Other biomolecular biometrics

1.2.1.   Hand bacteria identification

morphological  
Biometrics

2.1.    Face recognition

2.1.1.   Thermal infrared face recognition
2.1.2.   Near-infrared face recognition
2.1.3.   Video-based face recognition

2.1.4.   Image-based face recognition
2.1.5.   3D face recognition

2.2.    Friction ridge recognition

2.2.1.   Thermal friction ridge recognition
2.2.2.   Near-infrared friction ridge 

recognition

2.2.3.   3D friction ridge recognition
2.2.4.   Contactless friction ridge 

recognition

2.2.5.   Contact-based friction  
ridge recognition
2.2.6.   Fingerprint recognition
2.2.7.   Palmprint recognition
2.2.8.   Footprint recognition
2.2.9.   Finger-knuckle-print recognition

2.3.    Iris recognition

2.4.    Vascular pattern recognition

2.3.1.   Iris recognition in the NIR spectrum
2.3.2.   Iris recognition in the visible spectrum
2.3.3.   Iris recognition at a distance

2.4.1.   Retina recognition
2.4.2.   Sclera/Episclera recognition
2.4.3.   Finger vein recognition

2.4.4.   Palm vein recognition
2.4.5.   Back-of-hand vascular recognition
2.4.6.   Wrist vein recognition

2.5.    Physiological signals biometrics

2.5.1.   Heart-rate variability
2.5.2.   Electrocardiographic signals
2.5.3.   Phonocardiographic signals

2.5.4.   Photoplethysmographic signals
2.5.5.   Encephalographic signals
2.5.6.   Acoustic properties of the ear canal

2.6.    Hand geometry  
recognition

2.6.1.   Contact-based hand geometry recognition
2.6.2.   Contactless hand geometry recognition

2.7.    Other minor morphological biometrics

2.7.1.   Dental biometrics
2.7.2.   Tongueprint recognition
2.7.3.   Ear print recognition
2.7.4.   Periocular recognition

2.7.5.   Skin texture recognition
2.7.6.   Cranial suture scanning
2.7.7.   Rugoscopy
2.7.8.   MMW and THz wave body imaging

Continued on pg.26

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTemsContinued from pg.25

Behavioural  
Biometrics

3.1.    Keystroke recognition

3.1.1.   Dynamic keystroke recognition
3.1.2.   Static keystroke recognition

3.2.    Gait recognition

3.2.1.   Gait recognition based on video sensors
3.2.2.   Gait recognition based on radar sensors
3.2.3.   Gait recognition based on floor sensors
3.2.4.   Gait recognition based on wearable sensors

3.3.    Handwriting recognition

3.3.1.   Dynamic handwriting recognition
3.3.2.   Static handwriting recognition

3.4.    Speaker recognition

3.4.1.   Text-dependent speaker recognition
3.4.2.   Text-independent speaker recognition
3.4.3.   Vocal resonance recognition

3.5.    Other minor  

behavioural biometrics

3.5.1.   Complex eye movement patterns  

(eye-tracking)

3.5.2.   Oculomotor plant characteristics

2.1.

Biomolecular biometrics

Molecular and soft biomolecular biometrics is an advancing field that involves the 
analysis of a person’s unique biological markers at a molecular level to ascertain identity. 

2.1.1.

DNA biometrics

Amongst the various biometric technologies suitable for the recognition of individu-
als, deoxyribonucleic acid (DNA) provides the most reliable results.27 This technology 
is intrinsically digital (in terms of biometric data acquisition and processing) and does 
not change during a person’s life. DNA recognition finds easier applications in foren-
sic science, probably due to the high invasiveness and low efficiency of related modal-
ities. However, other applications in biometric recognition have also been proposed. 
For example, portable human DNA analysers exist that can conduct DNA analyses in 
approximately 25 minutes.28 Although these systems are still unsuitable for applications 
in the field of access control and border checks, advances are being made to expedite 
the analysis process.  

2.1.1.1.

DNA phenotyping
DNA phenotyping is the process of predicting an organism’s phenotype (i.e. the set of 
its observable characteristics or traits) using only genetic information collected from 
genotyping or DNA sequencing. DNA phenotyping,29 in particular, refers to the pre-
diction of appearance traits of an unknown sample donor, or an unknown deceased 

27 

28 

29 

M. Hashiyada, “DNA biometrics”, Biometrics, IntechOpen, 2011.

S. Bhable, S. Kayte, R. Maher, J. Kayte and C. Kayte, “DNA Biometric”, IOSR Journal of VLSI and 
Signal Processing, vol. 5, pp. 82-85, 2015.

M. Kayser, “Forensic DNA Phenotyping: Predicting human appearance from crime scene mate-
rial for investigative purposes”, Forensic Science International: Genetics, vol. 18, pp. 33-48, 2015.

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2.1.1.3.

2.1.1.4.

(missing) person, directly from sampled biological materials. In general, biometric infor-
mation from DNA phenotyping can only provide investigative leads to trace unknown 
persons who are unidentifiable with current comparative DNA profiling.

DNA profiling
DNA profiling30 (also called DNA fingerprinting) is used to assist in the recognition of 
individuals by their respective DNA profiles. Although more than 99.1% of the genome 
is the same throughout the human population, the remaining 0.9% of human DNA 
shows variations between individuals. These variable DNA sequences, termed polymor-
phic markers, can be used to both differentiate and correlate individuals.

DNA sequencing
DNA sequencing31 is the process of determining the nucleic acid sequence (the order of 
nucleotides in DNA). One possible technology to perform DNA sequencing is capillary 
electrophoresis, which offers high resolution and high throughput, automatic opera-
tion, and data acquisition.

Techniques for collecting DNA samples
This group refers to all the techniques used for the acquisition of DNA samples. This 
relates strongly to the field of forensics, where saliva samples, swab samples, tape sam-
ples, etc.,32 may be used in order to collect DNA.

2.1.2.

Other biomolecular biometrics

2.1.2.1.

Hand bacteria identification
Bacteria communities found on the skin of the human hand have shown to be high-
ly diverse and to have a low percentage of similarity between individuals. Some stud-
ies focus on this type of biometrics, and some claim to recognise individuals with high 
accuracy.33

30 

31 

32 

33 

E. Giardina, “DNA Fingerprinting”, Brenner's Encyclopedia of Genetics (Second Edition), Elsevier, 
2013, pp. 356-359.

G. Murugesan and G. W. Procop, “Direct Genome Sequencing in Diagnostic Pathology”, Cell 
and Tissue Based Molecular Pathology, Churchill Livingstone, 2009, pp. 56-62.

J. Mitchell, R. Van Oorschot and K. Ballantyne, “Forensic trace DNA: a review. Investigative genet-
ics.”, Investigative Genetics, vol. 1, no. 14, 2010.

A. B. Holbert, “Hand bacteria as an identifier: a biometric evaluation”, Network Modeling Anal-
ysis in Health Informatics and Bioinformatics, vol. 4, no. 22, 2015.

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Morphological biometrics

Morphological biometrics refers to biometric recognition modalities relying on the iden-
tification of the shape, size and distinctive features of the human body.

2.2.1.

Face recognition

Facial  recognition  analyses  a  digital  image  or  a  video  frame,  using  one  of  three 
approaches:
• 

Global (holistic) approach34 uses the entire face as the input data, which are then 
projected onto a subspace of smaller dimension;
Local (feature-based) recognition approaches35 considers local features or areas of 
the face such as the eyes, nose or mouth. These are extracted first and then their 
locations and local statistics (geometric and/or appearance) are used to classify 
human faces according to well-deﬁned rules and statistics;
Hybrid approaches36 make use of the features of both techniques and have the 
potential to offer better performance.

• 

• 

Facial recognition may be performed in the visible37 or invisible spectrum of light. Infrared 
face recognition includes various approaches for recognising human faces using infra-
red (IR) imaging. This is commonly sub-divided into:38 near-infrared (NIR, 0.78-1.0 µm 
in wavelength) imaging; short-wavelength infrared (SWIR, 1-3 µm) imaging; mid-wave-
length infrared (MWIR, 3-8 µm) imaging; long-wavelength infrared (LWIR, 8-15 µm) 
imaging. NIR and SWIR, usually with active illumination, are sometimes called “reflect-
ed infrared” while passive MWIR and LWIR techniques are sometimes referred to as 
“thermal infrared” (thermal IR).

Thermal infrared face recognition
Face recognition based on images captured using imaging sensors operating in the 
thermal IR portion of the electromagnetic spectrum (i.e. thermal cameras), including 
MWIR and LWIR. The human face emits thermal infrared radiation39 detectable by ther-
mal cameras. Temperature variations on the surface of the face produce a heat pat-
tern, which can be captured as a 2D image (thermal image) and used for recognition. 
Thermal IR imagery is independent of illumination40 and only requires passive IR sensor 

34 

35 

36 

37 

38 

39 

40 

S. Karamizadeh, S. Abdullah and M. Zamani, “An Overview of Holistic Face Recognition”, Inter-
national Journal of Research in Computer and Communication Technology, vol. 2, pp. 738-741, 
2013.

P. N. Divyarajsinh and M. B. Brijesh, “Face Recognition Methods & Applications”, International 
Journal of Computer Technology & Applications, vol. 4, pp. 84-86, 2013.

G. Prince and W. Heena, “A review on facial recognition including local, holistic and hybrid 
approaches”, Journal of Natural Remedies, vol. 21, pp. 210-216, 2020.

D. Sliney, “What is light? The visible spectrum and beyond”, Eye, vol. 30, pp. 222-229, 2016.

K. B. Mrinal, S. Kankan, M. Sharmistha, M. Goutam, S. Ashim, Aniruddha, S. Nath, B. Debotosh, 
K. B. Dipak and N. Mita, “Thermal Infrared Face Recognition – A Biometric Identification Tech-
nique for Robust Security system”, Reviews, Refinements and New Ideas in Face Recognition, 
IntechOpen, 2011.

G. Bebis, “Face Recognition, Thermal”, Encyclopedia of Biometrics, Springer, Boston, MA., 2009.

S. Ashim, S. Aniruddha Nath, B. Debotosh, K. B. Dipak and N. Mita, “Thermal Infrared Face Rec-
ognition – A Biometric Identification Technique for Robust Security system”, Reviews, Refine-
ments and New Ideas in Face Recognition, IntechOpen, 2011.

2.2.1.1.

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2.2.1.3.

2.2.1.4.

technology, because thermal IR sensors measure the heat energy emitted from objects, 
not reflected light. More importantly, thermal IR emission can be viewed in any visi-
ble light conditions and is less subject to scattering and absorption by smoke or dust 
than visible light. Therefore, thermal imaging has great advantages in face recognition 
under low illumination conditions and even in total darkness (without the need for IR 
illumination), where visual face recognition techniques fail.

Near-infrared face recognition
Face recognition based on images captured using NIR sensors. Near-infrared-based 
face recognition41, as opposed to the conventional visible light-based, is an effective 
approach to overcome the impact of illumination changes on face recognition. Since 
NIR imaging is based on reflected light (i.e. is due to the reﬂective material properties), 
it uses a special purpose imaging acquisition hardware based on active IR sensor tech-
nology in which NIR lights illuminate the face from the near frontal direction, and an 
NIR camera captures front-lit NIR face images. This is similar to a camera flash, but the 
imaging is done in the invisible NIR spectrum. With such NIR face images, problems 
caused by uncertainties in uncontrollable environmental illumination are minimised, and 
difficulties in building the face matching engine are much alleviated. The NIR approach 
usually achieves significantly higher performance than the visible light approach.

Video-based face recognition
Video-based face recognition,42 applied within the visible spectrum of light, is a tech-
nique for recognising one or multiple persons present in a video. Given the input face 
video, a typical video-based face recognition approach combines the temporal char-
acteristics of facial motion with appearance changes. This often involves the temporal 
characterisation of faces.43 As the face moves, its appearance as captured in a video var-
ies due to changes in the pose, illumination conditions, expression, etc. The pattern of 
these variations is often specific to the individual, containing distinguishing information 
that can be used by video-based face recognition systems for improved performance.

Image-based face recognition
Image-based face recognition, applied within the visible spectrum of light, is the tech-
nique of recognising one or multiple persons present in a single image based on their 
facial characteristics. Generally, the first step in an image-based face recognition process 
is the detection of a face in the image. If a face is detected, the successive step con-
sists in locating its position within the image. Factors such as variations in illumination, 
facial expressions (such as smiling or yawning) or individuals’ appearances (presence 
of moustaches, glasses, etc.) all hinder face detection. Device-related factors, such as 
limited resolution, lighting artifacts, image ﬁle format or compression, can also lower 
the performance of the biometric system. After a face is detected in an image, its fea-
tures must be represented in vectorial form for biometric comparison.44 

41 

42 

43 

44 

S. Z. Li and A. K. Jain, “Face Recognition, Near-Infrared”, Encyclopedia of Biometrics, Springer, 
Boston, MA, 2009, pp. 352-355.

R. Chellappa, G. Aggarwal and K. S. Zhou, “Face Recognition, Video-Based”, Encyclopedia of 
Biometrics, Springer, Boston, MA, 2015, pp. 514-521.

S. Z. Li and A. K. Jain, “Temporal Characterization of Faces”, Encyclopedia of Biometrics, Spring-
er, Boston, MA, 2009, pp. 1327-1327.

C. Mejda, E. Akram, B. Wajdi and B. A. Chokri, “A Survey of 2D Face Recognition Techniques”, 
Computers, vol. 5, no. 21, 2016.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems2.2.1.5.

3D face recognition 
Face recognition technologies that make use of the three-dimensional features of the 
facial components acquired by using visible or near-infrared45 light illumination. Once 
the three-dimensional geometry of the human face is acquired, it is used to extract dis-
tinctive features on its surfaces, such as the contour of the eye sockets, nose and chin. 
Recognition is then based on matching the metadata extracted from the 3D shapes of 
the face. 3D face recognition is claimed to have the potential to achieve better accura-
cy than its 2D counterpart.46

2.2.2.

Friction ridge recognition

The skin of the palms of the hands and fingers, as well as of the soles of the feet and 
toes, is distinctly different from that of the rest of the body and is known as friction ridge 
skin in the biometric and forensic communities.47 This technological field includes the 
different biometric recognition modalities used to recognise individuals based on the 
unique geometrical features of the friction ridges of various parts of the human body.

Thermal friction ridge recognition 
Friction ridge recognition based on signals captured using thermal scanners capable of 
sensing the temperature differences on the contact surface in between friction ridges 
and valleys of the aforementioned body parts.48

Near-infrared friction ridge recognition 
Recognition that uses friction ridge biometric data captured using near-infrared light 
illumination. For example, near infrared palmprint images have been shown to be reli-
able for efficient recognition.49

3D friction ridge recognition
Biometric recognition technologies based on scanning technologies and image detec-
tion systems capable of acquiring the frictional ridges of one or multiple body parts (i.e. 
fingers, palms, feet or finger knuckles) and producing a three-dimensional representa-
tion of them in order to recognise an individual. For example, extracted features from 
3D palmprint data usually include depth and curvature of the palm lines and wrinkles 
on the palm surface.50

45 

46 

47 

48 

49 

50 

G. A. Atkinson, M. F. Hansen and M. L. Smith, “A efficient and practical 3D face scanner using 
near infrared and visible photometric stereo”, Procedia Computer Science, vol. 2, pp. 11-19, 2010.

Z. Eng, Y. Yick, Y. Guo, H. Xu, M. Reiner, T. Cham and S. Chen, “3D faces are recognized more 
accurately and faster than 2D faces, but with similar inversion effects”, Vision Research, vol. 138, 
pp. 78-85, 2017.

A. R. Hicklin, “Anatomy of Friction Ridge Skin”, Encyclopedia of Biometrics, Springer US, 2009, 
pp. 23-28.

J.-s. Han, Z.-y. Tan, K. Sato and M. Shikida, “Thermal characterization of micro heater arrays on 
a polyimide film substrate for fingerprint sensing applications”, Journal of Micromechanics and 
Microengineering, vol. 15, pp. 282-289, 2015.

A. Meraoumia, S. Chitroub and B. Chitroub, “Are infrared images reliable for palmprint based 
personal identification systems?”, Saudi International Electronics, Communications and Pho-
tonics Conference, 2013.

D. Zhang and V. Kanhangad, “Palmprint, 3D”, Encyclopedia of Biometrics, Springer, Boston, MA, 
2009, pp. 1037-1042.

2.2.2.1.

2.2.2.2.

2.2.2.3.

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2.2.2.5.

2.2.2.6.

2.2.2.7.

2.2.2.8.

Contactless friction ridge recognition
Recognition modalities in which the friction ridge mark signature is acquired without 
direct contact of the relevant body part with a sensing surface, mostly employing the 
video or image acquisition of fingerprints, palmprints, footprints and knuckle prints. Con-
tactless friction ridge recognition is characterised by low constrained acquisition process-
es, which determines high levels of usability and hence, user acceptance. Further, finger/
palm/foot/knuckle images acquired by a contactless sensor exhibit no deformation.51 

Contact-based friction ridge recognition
Recognition modalities in which the friction ridge mark signature is acquired through 
the contact of the body part with an acquiring surface. For example, contact-based 
palmprint capture may be performed by asking the users to put their hands on a pla-
nar surface where their fingers are typically restricted by pegs. The biometric samples 
captured by a Contact-based friction ridge recognition system can be directly used for 
feature extraction, while the corresponding contactless friction ridge images require 
further pre-processing.51 

Fingerprint recognition
Methods and technologies for the recognition of individuals based on the unique geo-
metrical features of the friction ridges of human fingers. A fingerprint52 is the represen-
tation of the dermal ridges of a finger that form through a combination of genetic and 
environmental factors: the genetic code in DNA gives general instructions on the way 
the skin should form in a developing foetus, but the specific way in which it forms is a 
result of random events such as the exact position of the foetus in the womb at a partic-
ular moment. This is the reason why even the fingerprints of identical twins are different.

Palmprint recognition
Methods and technologies for the recognition of individuals based on the unique geo-
metrical features of the friction ridges of human palms. A palmprint53 is formed by the 
skin patterns of the inner surface of the human hand from the wrist to the root of the 
fingers and is rich in physical characteristics such as lines, points, and textures, which 
provide stable and distinctive information sufficient for recognition. Palmprint recog-
nition is a relatively new biometric technology and recognition systems use palmprint 
features that may or may not be observable to the naked eye.

Footprint recognition
Methods and technologies for recognising individuals based on the unique geometri-
cal features of the friction ridges of the feet. Prior work on footprint biometrics mainly 
explored the use of foot shape features such as length, width, major axis, minor axis 
and centroid, but proved unreliable due to similarity in those features amongst indi-
viduals. Instead, footprint texture features coming from creases of the soles of the feet 
and minutiae of toes are unique and permanent like palmprint texture features. Recent-

51 

52 

53 

J. Priesnitz, C. Rathgeb and N. Buchmann, “An overview of touchless 2D fingerprint recognition”, 
EURASIP Journal on Image and Video Processing, vol. 2021, no. 8, 2021.

D. Maltoni, “Fingerprint Recognition, Overview”, Encyclopedia of Biometrics, Springer, Boston, 
MA, pp. 510-513.

D. Zhang and L. L. Liu, “Palmprint Features”, Encyclopedia of Biometrics, Springer, Boston, MA, 
2009, pp. 1043--1049.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems2.2.2.9.

ly, various recognition techniques based on the extraction and classification of those 
features have been investigated.54

Finger-knuckle-print recognition
Methods and technologies for the recognition of individuals based on the unique geo-
metrical features of the friction ridges of human finger-knuckles. The finger-knuckle 
surface55 is defined as the skin pattern present on the backs of the fingers. This region 
has three joints: the metacarpophalangeal joint which joins the finger to the hand; the 
proximal interphalangeal (PIP) joint in the middle of the finger; and the final distal joint. 
The presence of these joints in the finger dorsum surface forms the flexion shrinks on 
the outer region of the skin, which creates the dermal patterns consisting of lines, wrin-
kles, contours etc. The pattern generated by the PIP joint on the finger back region is 
referred to as finger-knuckle-print (FKP). The area of the captured finger-knuckle-print 
is very small as compared to the area captured for Palmprint recognition, and also has 
highly unique features well suited for a biometric recognition system.

2.2.3.

Iris recognition

The iris is a thin, circular structure in the eye that controls the diameter and size of the 
pupils. The iris consists of muscle tissues that cause the pupil to contract and dilate. The 
back surface is covered by a layer of pigmented epithelial tissue, which gives an eye its 
distinctive colour. Iris recognition is the field of biometrics that deals with the recogni-
tion of individuals through the textural features of the iris muscular patterns.

Iris recognition in the NIR spectrum
Iris recognition based on images captured using near-infrared (NIR) illumination. Due to 
the presence of melanin (the pigment which attributes the iris its appearance) absorp-
tion by the iris is significant for light within the visible spectrum while it is almost neg-
ligible for infrared wavelengths.56 High reflectance (i.e. low absorption) enables good 
visibility of the iris texture details even for dark irises. For this reason, most commercial 
iris scanning cameras acquire images using NIR illumination.57

Iris recognition in the visible spectrum
Biometric recognition technology based on images of the iris captured in the visible 
spectrum of light. This presents many challenging aspects, especially in the case of indi-
viduals with dark irises58 (caused by higher melanin pigmentation and collagen fibrils) 
because the unique pattern of the iris is not clearly observable under visible light.

54 

55 

56 

57 

58 

R. Kushwaha and N. Nain, “Person Identification Using Footprint Minutiae”, Advances in Intel-
ligent Systems and Computing, vol. 1024, 2020.

K. Usha, “Personal recognition using finger knuckle shape oriented features and texture analy-
sis”, Journal of King Saud University – Computer and Information Sciences, vol. 28, pp. 416-431, 
2016.

M. Trokielewicz and B. Ewelina, “Cross-spectral Iris Recognition for Mobile Applications using 
High-quality Color Images”, Journal of Telecommunications and Information Technology, pp. 
91-97, 2016.

M. Hosseini, B. Araabi and H. Soltanian-Zadeh, “Pigment Melanin: Pattern for Iris Recognition”, 
IEEE Transactions on Instrumentation and Measurement, VOL. 59, NO. 4, APRIL 2010, vol. 59, 
no. 4, pp. 792-804, 2010.

K. B. Raja, R. Raghavendra and C. Busch, “Visible iris imaging: A novel imaging solution for 
improved iris recognition”, IEEE International Conference on Imaging Systems and Techniques 

2.2.3.1.

2.2.3.2.

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Iris recognition at a distance
This modality refers to capturing iris images metres away from the subject and analysing 
them. With the right detection equipment, Iris recognition at a distance might be imple-
mented even for a person walking.59 This would enhance the traveller’s experience at 
border checks, especially where a large number of people move through a bottleneck 
(e.g. a border check point), reducing the need for user cooperation and achieving low 
intrusiveness, and thus high acceptance and transparency.

2.2.4.

Vascular pattern recognition

2.2.4.1.

Vascular pattern recognition, also commonly referred to as Vein pattern recognition, 
uses a powerful light source (usually near-infrared light60) to acquire images of blood 
vessels. Researchers have determined that the vascular pattern of the human body is 
unique to a specific individual and does not change as people age. Two main acquisi-
tion approaches exist to Vascular pattern recognition: reflection and transmission modes. 
In the first, NIR illumination is emitted towards the body part of interest (retina, finger, 
palm, etc.) and the reflected light is captured by a sensor. The traces where veins occur 
are captured as darker parts because veins absorb more near-infrared illumination 
than their surrounding elements due to the high absorption of haemoglobin. In the 
second, the illumination device and the sensor must be placed opposite one another 
so that the sensor may capture an image formed by the light transmitted through the 
skin of the person.61

Retina recognition
The retina is the innermost, light-sensitive layer of tissue in human eyes, which serves 
a function analogous to that of the film or image sensor in a camera. The optics of the 
eye create a focused two-dimensional image of the visual world on the retina, which 
translates that image into electrical neural impulses to the brain to create visual percep-
tion. It has two distinct vascular networks, the choroidal network and the retinal network, 
whose unique patterns enable biometric recognition. Generally, subjects are request-
ed to position their eyes in front of a capture system at a distance ranging from 8 cm 
to one metre. The person must then look at a series of markers, viewed through the 
eyepiece, and line them up. During this process, the retina is scanned by near-infrared 
radiation and the pattern of the blood vessels is captured. This technology is not to be 
confused with other ocular-based biometric technologies such as iris recognition and 
scleral/episcleral scanning.62

2.2.4.2.

Sclera/episclera recognition
This is a biometric technique that uses unique patterns on a person’s sclera blood ves-
sels. The sclera, also known as the white of the eye, is the opaque, fibrous and pro-

(IST), 2015.

59 

H. Proença, “Iris Recognition in the Visible Wavelength”, Handbook of Iris Recognition, Springer, 
London, 2013, pp. 151-169.

60  W. Wu, “Selection of Typical Wavelength for Palm Vein Recognition”, Acta Optica Sinica, vol. 32, 

no. 12, pp. 1-7, 2012.

61 

62 

A. Uhl, “State of the Art in Vascular Biometrics”, Handbook of Vascular Biometrics. Advances in 
Computer Vision and Pattern Recognition., Springer, Cham, 2020.

Y. Seto, “Retina Recognition”, Encyclopedia of Biometrics, Springer, Boston, MA, 2009, pp. 1128-
1130.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTemstective outer layer of the human eye that surrounds the iris. The human sclera mostly 
contains collagen and some crucial elastic fibres and is formed from four layers which, 
from outermost to innermost, are: episclera, stroma, lamina fusca, and endothelium.63 
The vasculature of the sclera has several desirable characteristics that make it appeal-
ing for biometric recognition systems:64 unlike the iris and the periocular region, the 
sclera has a rich vascular structure that is considered unique for each individual and 
even differs between identical twins; the vein patterns are discernible despite potential 
eye redness and also in the presence of contact lenses that may adversely affect iris 
recognition systems. The vasculature is relatively stable over time and difficult to forge, 
so sclera recognition systems are commonly considered to be resistant to presenta-
tion attacks and spoofing.

Finger vein recognition
Scanning technologies aiming to recognise an individual by their fingers’ unique vas-
cular pattern. Finger vein network patterns are visible under NIR or visible illumination.65 
In this definition, the term finger includes all 5 fingers of the hand.

Palm vein recognition
Scanning technologies aiming to recognise an individual by their palms’ unique vascular 
pattern. These palm patterns66 are normally captured under NIR illumination using the 
reflection method. Palm veins emerge as dark traces in the captured images because 
they absorb a high percentage of the near-infrared illumination. With this method, con-
tactless pattern-capturing and recognition systems can be created.

Back-of-hand vascular recognition
Back-of-hand (or dorsal hand) vascular recognition67 is based on the subcutaneous 
vascular network on the back of the hands. According to large-scale experiments, this 
pattern of blood vessels is unique to everyone, even amongst identical twins. Dorsal 
hand vein recognition is known for high accuracy, stability, and resistance to attacks.68 
The crucial factor in achieving accurate recognition is the quality of the vein image that 
can be captured in real-life scenarios. These images are captured using similar prin-
ciples to those for palm veins: the back of the hand is illuminated using near-infrared 
radiation and the reflected light is used to acquire an image of its vascular pattern. The 
deoxidised haemoglobin in blood vessels absorbs more infrared rays than surrounding 
tissues and causes the blood vessels to appear as black patterns in the resulting image 
captured by an NIR camera.

63 

64 

65 

66 

67 

68 

B. Cassin and S. Solomon, “Dictionary of Eye Terminology”, Triad Publishing Company, 1990.

M. Vitek, P. Rot, V. Štruc and P. Peer, “A comprehensive investigation into sclera biometrics: a 
novel dataset and performance study”, Neural Computing and Applications, vol. 32, p. 17941–
17955, 2020.

N. Miura, “Technology and Future Prospects for Finger Vein Authentication Using Visible-light 
Cameras”, Hitachi Rev, vol. 67, 2018.

T. Aoki and T. Shinzaki, “Palm Vein”, Encyclopedia of Biometrics, Springer, Boston, MA, 2009, pp. 
1195-1202.

A. H. Choi, “Back-of-Hand Vascular Recognition”, Encyclopedia of Biometrics, Springer, Boston, 
MA, 2009, pp. 55-60.

R. Raghavendra, J. Surbiryala and C. Busch, “Hand Dorsal Vein Recognition: Sensor, Algorithms 
and Evaluation”, in 2015 IEEE International Conference on Imaging Systems and Techniques 
(IST), 2015.

2.2.4.3.

2.2.4.4.

2.2.4.5.

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Wrist vein recognition
Technologies aiming to recognise an individual by their wrists’ unique vascular pattern, 
which has similar usability as palm vein and finger vein modalities. The wrist vein vari-
ant, with wider veins, provides clear visualisation and definition of a person’s unique 
vein patterns.69

2.2.5.

Physiological signal biometrics

2.2.5.1.

2.2.5.2.

Physiological signals are characteristics of individuals that do not rely on morphological 
characteristics of the human body, but rather refer to those signals that can be acquired 
and monitored to assess a person’s clinical state. The same signals can be used for rec-
ognition. However, in general, the acquisition modalities of physiological signals can be 
uncomfortable and may require expensive equipment.70 Part of the current research 
is focused on the combination of multiple physiological signals to reduce the vulner-
ability of biometric systems based on these signals to external attacks, as their level of 
security can still be regarded as insufﬁcient.

Heart-rate variability
Heart-rate variability (HRV) is an intrinsic property of the heart and an active research 
domain within the medical research community for the last two decades.71 Its application 
in biometrics is still in its infancy. Irvine et al.72 proposed one of the very few attempts 
speciﬁcally aimed at HRV-based biometric recognition, but the actual techniques used 
and the results obtained are not fully known.

Electrocardiographic signals
Electrocardiographic (ECG) signals describe the electrical activity of the heart over time, 
recorded with electrodes attached to the surface of the body. Traditionally, physicians 
use ECG characteristics to gain insights into the heart’s condition, usually with comple-
mentary tests required in order to finalise a diagnosis. From a biometrics perspective, 
it was demonstrated73 that the ECG has sufficient details for applications in biometric 
recognition. The advantages of using ECG signals for biometric recognition can be sum-
marised as universality, permanence, uniqueness, robustness to attacks, live detection, 
continuous recognition and data minimisation.

2.2.5.3.

Phonocardiographic signals
Phonocardiographic (PCG) signals are the sounds and murmurs made by the heart 
during a cardiac cycle. A phonocardiogram is a plot of a high-fidelity recording of PCG 

69 

70 

71 

72 

73 

R. Garcia-Martin and R. Sanchez-Reillo, “Wrist Vascular Biometric Recognition Using a Portable 
Contactless System”, Sensors, vol. 20, p. 1469, 2020.

M. Moreno Revelo, M. Ortega Adarme, D. Peluffo Ordoñez, K. Alvarez Uribe and M. Becerra, 
“Comparison Among Physiological Signals for Biometric Identification”, Lecture Notes in Com-
puter Science, vol. 10585, 2017.

A. Nazneen, S. Tharewal, V. Kale, A. Bhalerao and K. Kale, “Heart Based Biometrics and use of 
Heart Rate Variability in Human Identification Systems”, Advanced Computing and Systems for 
Security, vol. 395, Springer India, 2015, pp. 15-29.

J. Irvine, B. Wiederhold, L. Gavshon, S. Israel, S. McGehee, R. Meyer and M. Wiederhold, “Heart 
rate variability: a new biometric for human identiﬁcation”, International Conference on Artificial 
Intelligence (IC-AI'2001), 2001.

A. Foteini, G. Jiexin and H. Dimitrios, “Heart Biometrics: Theory, Methods and Applications”, 
Biometrics, IntechOpen, 2011, pp. 199-217.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTemssignals made using a machine called a phonocardiograph, while phonocardiography is 
the recording of the PCG signals. The use of these signals for biometric recognition 
represents a novel approach74 and has the potential to reduce the risks of vulnerability 
faced by other biometric systems.75

Photoplethysmographic signals
Photoplethysmographic (PPG) signals are optically obtained signals (e.g. by illuminating 
the skin using a light-emitting diode and detecting the intensity changes in the reflect-
ed light) that can be used to detect blood volume changes in the microvascular bed of 
tissue. The corresponding temporal plot is called the photoplethysmogram. Compared 
to other commonly used physiological signals, PPG signals offer important advantag-
es.76 First, the acquisition of PPG signals may be performed in a more comfortable 
manner, as the sensor placement does not require the use of gels, and the acquisition 
devices are often widely diffused in medical applications. For example, PPG biometric 
samples can be captured by using pulse oximeters, which are non-invasive devices for 
monitoring haemoglobin saturation and are usually attached to the earlobe or finger-
tip. Another advantage is that pulse oximeters are usually less expensive and smaller 
than the acquisition devices used for ECG, EEG, and PCG signals. 

Encephalographic signals
An emerging approach in physiological biometrics is cognitive biometrics,77 which relies 
on the measurement of the brain’s response to stimuli performed through several devic-
es, including measurements performed by using Electroencephalography (EEG) systems. 
EEG is an electrophysiological monitoring method to record the electrical activity of 
the brain. It is typically executed by placing a set of electrodes along a person’s scalp.

Acoustic properties of the ear canal
The ear canal is a resonant system, which together with the pinna (the visible part of 
the ear that is outside the head) provides rich acoustic features. In a coarse approxi-
mation, it is a one-dimensional system that resonates at one-quarter of the acoustic 
wavelength. The resonance frequency of the ear canal will typically be around 2500 Hz, 
but this will vary from person to person, as it depends on the lengths and shapes (cur-
vatures) of the pinna and ear canal which have dimensions that vary from millimetres 
to a few centimetres. The acoustic properties of the ear canal can be used as a bio-
metric characteristic, can be measured relatively easily with an inexpensive sensor, and 
feature vectors can be derived with little effort.78

74 

75 

76 

77 

78 

A. Singh and A. K. Singh, “Biometric identification using phonocardiogram”, thesis submitted 
for partial fulfillment of the requirements for the degree of Bachelor of Technology in Electron-
ics and Communication Engineering – National Institute of Technology, Rourkela, Orissa, India, 
2011.

T. Meitei, A. Sinam and S. Majumder, “PCG based biometric”, Handbook of Research on Infor-
mation Security in Biomedical Signal Processing, 2020, pp. 1-25.

A. Bonissi, R. D. Labati, L. Perico, R. Sassi, F. Scotti and L. Sparagino, “A Preliminary Study on 
Continuous Authentication Methods for Photoplethysmographic Biometrics”, 2013 IEEE Work-
shop on Biometric Measurements and Systems for Security and Medical Applications, 2013.

R. J. Rodriguez, “An Electroencephalogram (EEG) Based Biometrics Investigation for Authenti-
cation: A Human-Computer Interaction (HCI) Approach”, NSUWorks, 2015.

T. Akkermans, K. T.A.M. and S. D.W.E., “Acoustic Ear Recognition”, Lecture Notes in Computer 
Science, vol. 3832, 2005.

2.2.5.4.

2.2.5.5.

2.2.5.6.

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Technology ForesighT on BiomeTrics For The FuTure oF Travel2.2.6.

Hand geometry recognition

Hand geometry is a widely accepted biometric characteristic that allows the recogni-
tion of individuals from the shape of their hands. Hand geometry readers measure the 
individual’s hand along many dimensions including height, width, deviation and angle, 
and compare those measurements to a reference sample. Although these measure-
ments are not very distinctive amongst people, hand geometry can be very useful for 
biometric recognition. Major advantages of hand geometry systems include: simple 
requirements in imaging resolution (hand features can be extracted from low-resolu-
tion images); the ability to operate under harsh environmental conditions (hand geom-
etry biometrics are immune, for example, to dirt on the hand and other external fac-
tors); and low data-storage requirements. In addition, hand geometry acquisition and 
recognition are extremely fast. This technique is widely accepted, and the recognition 
process involves simple data processing.79

Contact-based hand geometry recognition
Contact-based (also known as platform-based) hand geometry scanning uses ﬂat plat-
forms with which the hands must be placed in contact, facilitating not only the acqui-
sition procedure but also the segmentation and post-acquisition feature extraction. 
Depending on the use of pegs or pins to restrict the hand’s degrees of freedom, con-
tact-based solutions may be further sub-divided into constrained and unconstrained.80

Contactless hand geometry recognition
In contactless hand geometry recognition technological solutions, neither pegs and 
pins nor platforms are required for the hand geometry acquisition, which may be con-
ducted using static video cameras,81 web cams,82 or other digital contactless image-cap-
turing devices.

2.2.6.1.

2.2.6.2.

2.2.7.

Other minor morphological biometrics

As ascertained during the study, other morphological biometrics exist that are less 
relevant than the above-mentioned primary ones to the fields of border checks and 
access control. For the sake of completeness, these minor morphological biometrics 
were included in this taxonomical group.

2.2.7.1.

Dental biometrics
Dental biometrics uses information about dental structures for the recognition of indi-
viduals. This methodology is mainly applied to the identification of individuals in foren-
sic analyses. The process of dental recognition consists of measuring dental features, 
labelling individual teeth with tooth indices, and matching these dental features with 

79 

80 

81 

82 

D. Zhang and V. Kanhangad, “Hand Geometry Recognition”, Encyclopedia of Cryptography and 
Security, Springer, Boston, MA, 2011, pp. 529-531.

M. Bača, P. Grd and T. Fotak, “Basic Principles and Trends in Hand Geometry and Hand Shape 
Biometrics”, New Trends and Developments in Biometrics, IntechOpen, 2012.

X. Jiang, “New directions in contact free hand recognition”, International Conference in Image 
Processing: Proceedings of the IEEE International Conference in Image Processing, 2007.

A. Morales, M. A. Ferrer, J. B. Alonso and C. M. Travieso, “Comparing infrared and visible illu-
mination for contactless hand based biometric scheme”, 2008 42nd Annual IEEE International 
Carnahan Conference on Security Technology, pp. 191-197, 2008.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTemsbiometric samples. Commonly used dental features are based on tooth morphology 
(shape) and appearance (grey level). Dental radiographs are the major source for obtain-
ing morphological dental features.83 

Tongueprint recognition
Biometric technology for automatically recognising a person using tongue information. 
As the tongue can be extended out from the body for inspection, shape and texture 
information can be acquired from images (e.g. using a machine vision system) as a 
tongue print for the recognition process. Unlike other biometrics (e.g. face and finger-
print), the tongue is an internal organ and well protected in the mouth, so it is basical-
ly immune to forgery. This is advantageous for protecting users’ privacy and security.84 
Furthermore, the involuntary squirming of the tongue is not only convincing proof that 
the subject is alive, but also a characteristic for recognition. The tongue can also pres-
ent both static and dynamic features for recognition.

Ear print recognition
Ear print analysis is mainly used as a means of forensic identification intended as a char-
acteristic similar to fingerprinting. An ear print is a two-dimensional reproduction of the 
parts of the outer ear that have touched a specific surface (most commonly the helix, 
antihelix, tragus, and antitragus). Although ear prints do not have powerful distinctive-
ness information, they are useful as forensic evidence and are a promising soft biometric.85

Periocular recognition 
The full region around the eye, including the sclera, eyelids, lashes, brows, and skin, is 
known as the periocular region,86 and can be acquired non-intrusively and used as a 
biometric characteristic.87 Periocular recognition offers advantages over full-face recog-
nition as it is least affected by expression variations, ageing effects, and changes due 
to the growth of facial hair. Moreover, this biometric technology may perform better in 
the case of extreme pose changes when only one eye is completely visible.

Skin texture recognition
Skin texture is the surface texture pattern of any part of the human body with bare 
skin (e.g. face, hand and palm). In the context of biometrics, this term commonly refers 
to the technologies and methods of face recognition using highly detailed facial skin 
texture in high-resolution images. A person’s skin texture pattern is, to some extent, a 

83 

84 

85 

86 

87 

H. Chen and A. K. Jain, “Dental Biometrics”, Encyclopedia of Biometrics, Springer, Boston, MA, 
2015, pp. 343-351.

S. Z. Li and A. K. Jain, “Tongue-Print Recognition”, Encyclopedia of Biometrics, Springer, Boston, 
MA, 2009, pp. 1340-1340.

A. Morales, M. Diaz, G. Llinas-Sanchez and M. A. Ferrer, “Earprint recognition based on an 
ensemble of global and local features”, International Carnahan Conference on Security Tech-
nology (ICCST), 2015.

K. Hernandez-Diaz, F. Alonso-Fernandez and J. Bigun, “Periocular Recognition Using CNN Features 
Off-the-Shelf”, International Conference of the Biometrics Special Interest Group (BIOSIG), 2018.

M. Uzair, A. Mahmood, A. Mian and C. McDonald, “Periocular Biometric Recognition using Image 
Sets”, IEEE Workshop on the Applications of Computer Vision (WACV), pp. 246-251, 2013.

2.2.7.2.

2.2.7.3.

2.2.7.4.

2.2.7.5.

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Technology ForesighT on BiomeTrics For The FuTure oF Travel2.2.7.6.

2.2.7.7.

2.2.7.8.

unique physical trait and is distinguishable from those of others, and thus can be used 
for biometric recognition.88

Cranial suture scanning
Cranial sutures are fibrous joints providing malleability to the head that allows the growth 
of the brain during its early development89 and can be used for biometric recognition. 
More specifically, cranial sutures in preadolescent skulls are known to present distinc-
tive patterns, whose significance reduces in adulthood.90 

Rugoscopy
The palatal rugae are asymmetrical and irregular elevations of the mucosa located in 
the anterior third of the palate. The study of palatal rugae patterns for human identifi-
cation is described as rugoscopy.91 Palatal rugae were shown to be highly individualis-
tic and unique, and maintain consistency in shape throughout life. In forensic dentist-
ry, palatal rugae patterns can lead to important information and help in recognition.

MMW and THz wave body imaging
The use of millimetre waves (MMW) and submillimetre waves (SMW, also called THz 
radiation) within body imaging systems is an active field of research due to various inter-
esting properties. Penetration through clothing and other nonpolar dielectric materi-
als, even at standoff ranges, is one of the most important abilities of MMW and SMW. 
Therefore, security screening (e.g. detection of concealed weapons under clothing), 
non-destructive inspection of goods and medical and biometric imaging are the most 
relevant applications of MMW/SMW imaging. In the biometrics domain, only works for 
presentation attack detection in the THz range are currently known.92 

88 

89 

90 

91 

92 

X. Zhu, Z. Lei and S. Z. Li, “Skin Texture”, Encyclopedia of Biometrics, Springer, Boston, MA, 2009, 
pp. 1229-1232.

J. Crompton and J. Black, “Chapter 28 – Craniofacial abnormalities”, Pediatric Ophthalmology 
and Strabismus (Fourth Edition), W.B. Saunders, London, 2013, pp. 243-264.

P. T. Jayaprakash, “Skull Sutures: Radiographic Contour of Wormian Bone as an Individualizing 
Epigenetic Marker”, Journal of the Canadian Society of Forensic Science, vol. 30, pp. 39-47, 1997.

M. Rezwana Begum, “Rugoscopy: Human identification by computer-assisted photographic 
superimposition technique”, Journal of forensic dental sciences, vol. 5, pp. 90-95, 2013.

N. Palka and M. Kowalski, “Towards Fingerprint Spoofing Detection in the Terahertz Range”, 
Sensors, vol. 20, no. 12, p. 3379, 2020.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems2.3.

Behavioural biometrics

Behavioural biometrics is a class of biometrics based on various human actions as 
opposed to physical characteristics.93 It is related to the measurement of uniquely iden-
tifiable and measurable patterns in human activities. Within the scope of this study, this 
group of biometric technologies was considered only in relation to its possible applica-
tion to the recognition of individuals and access control. Possible usages for emotional 
analysis or the prediction of human intensions were not considered.

2.3.1.

Keystroke recognition

Keystroke dynamics is a behavioural biometric characteristic suitable for the automat-
ed recognition of an individual based on their own unique typing rhythm. It refers to 
the detailed timing information and patterns describing how a person is typing on a 
digital device keyboard. Unlike morphological biometric characteristics, such as finger-
print or iris, where specialised sensors are necessary to collect the biometric data from 
the captured subject, keystroke dynamics can be detected using off-the-shelf physical 
computer keyboards or virtual keyboards in smartphones and PDAs. Thus, keystroke 
dynamics represents a low-cost solution and is easily deployable in a variety of scenari-
os. On the other hand, one of the major drawbacks is that a person’s typing varies sub-
stantially during the day and from day to day, and may be affected by multiple exter-
nal factors. Although other measurements are conceivable, patterns used in keystroke 
dynamics are derived mainly from the two events that make up a keystroke:94 Key-Down 
and Key-Up. The Key-Down event takes place at the initial depression of a key, and the 
Key-Up occurs at the subsequent release of that key. Various unique features are then 
calculated based on the intra-key and inter-key timing variations between these events.

Dynamic keystroke recognition
Keystroke recognition modality that uses the keystroke dynamics acquired when the bio-
metric capture subject types any text. This is often used for continuous recognition, i.e. 
to continuously ensure the validity of an initial recognition throughout the interaction 
period of the subject with the keyboard. Such a system will continuously monitor the typ-
ing behaviour of a user so that the system can be locked if a different user is detected.95

Static keystroke recognition
Keystroke recognition modality that uses the keystroke dynamics acquired when the bio-
metric capture subject types a predefined text or a password. Static Keystroke recognition is 
in general used to recognise the individual at the initial instance of interaction with the bio-
metric system (for example, to supplement the password in order to avoid password shar-
ing) providing a more robust user verification than simple passwords, but does not provide 
continuous security, as it cannot detect substitution of the user after the initial verification.96 

93 

94 

95 

96 

S. Z. Li and A. K. Jain, “Behavioral Biometrics”, Encyclopedia of Biometrics, Springer, Boston, MA, 
2009, p. 62.

N. Bartlow, “Keystroke Recognition”, Encyclopedia of Biometrics, Springer, Boston, MA, 2009, 
pp. 877-882.

P. Bours, “Continuous keystroke dynamics: A different perspective towards biometric evalua-
tion”, Information Security Technical Report, vol. 17, pp. 36-43, 2012.

F. Monrose and A. Rubin, “Keystroke dynamics as a biometric for authentication”, Future Gen-
eration Computer Systems, pp. 351-359, 2000.

2.3.1.1.

2.3.1.2.

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Technology ForesighT on BiomeTrics For The FuTure oF Travel2.3.2.

Gait recognition

2.3.2.1.

2.3.2.2.

2.3.2.3.

2.3.2.4.

Gait allows to recognize individuals by their walking style (or manner) and pace. Gait 
has several advantages compared to other biometric characteristics: in most modali-
ties, Gait recognition is non-intrusive, does not require cooperation from the individual 
and can function at moderate distances from the subject.97

Gait recognition based on video sensors
In this modality, gait is captured from a distance using a video camera. Video and image 
processing techniques are employed to extract gait features for the recognition pro-
cess. Most of the Machine Vision (MV)-based Gait recognition algorithms are based on 
using the human silhouette: the image background is removed, and the silhouette of 
the biometric captured subject is extracted and analysed for recognition. The prima-
ry advantage of video-based Gait recognition compared to other modalities is that it 
is captured from a distance when other biometric characteristics are not accessible.98

Gait recognition based on radar sensors
Detailed radar signal processing can reveal many characteristics of human motions and 
of the human body morphology, including gait characteristics. The transmitted radar 
signal is insensitive to the environmental light conditions of day and night, while smoke, 
dust, and fog only slightly reduce the signal. Therefore, radar can be effectively used in 
situations where other sensors perform poorly or cannot be used at all.99 Radar signals 
also penetrate most clothing, preventing disguises from being effective. Using radar-
based Gait recognition in conjunction with other biometric recognition technologies 
can help to reduce the susceptibility of the combined system to poor visibility condi-
tions and intentional deception.

Gait recognition based on floor sensors
In this modality, a set of sensors or force plates are installed on the ﬂoor that enable 
gait biometric feature extraction when a person walks on them.98 Floor sensor-based 
Gait recognition systems are usually installed along corridors leading to check-points.

Gait recognition based on wearable sensors
In this modality, gait biometric features are acquired using wearable Motion Record-
ing (MR) sensors, which can be worn or attached to various places on the human body. 
Examples of sensors can be accelerometers, gyro sensors, force sensors and bend sen-
sors, capable of measuring various characteristics of walking.100 Typically, the acceler-
ation of gait, recorded by the MR sensor, is used for subject recognition. One of the 
main advantages of WS-based Gait recognition over several other biometric technolo-

97 

98 

99 

R. Chellappa, A. Veeraraghavan and N. Ramanathan, “Gait Biometrics, Overview”, Encyclopedia 
of Biometrics, Springer, Boston, MA, 2009, pp. 628-633.

D. Gafurov, “A survey of biometric gait recognition: Approaches, security and challenges”, Pro-
ceedings of the Annual Norwegian Computer Science Conference, 2007.

D. Tahmoush and J. Silvious, “Radar micro-Doppler for long range front-view gait recognition”, IEEE 
3rd International Conference on Biometrics: Theory, Applications, and Systems, BTAS, pp. 1-6, 2009.

100 

D. Gafurov and E. Snekkenes, “Gait Recognition Using Wearable Motion Recording Sensors”, 
EURASIP Journal on Advances in Signal Processing, vol. 2009, no. 7, pp. 1-16, 2009.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTemsgies is its unobtrusive acquisition process. The WS-based approach has been recently 
proposed for device protection and user recognition in mobile and portable devices.101

2.3.3.

Handwriting recognition

2.3.3.1.

2.3.3.2.

Handwriting recognition is the process of recognising the author of a text from their 
handwriting style, and includes (and mostly refers to) signature recognition. Handwrit-
ing recognition can be implemented in several ways, and applied to a generic text or 
to a specific predefined text (usually a signature).

Dynamic handwriting recognition
In this approach (also known as online handwriting recognition), the biometric capture sub-
ject writes a text on a digitising screen, which acquires the signature in real time. Another 
possibility is the acquisition by means of stylus-operated PDAs. Some systems also oper-
ate on smartphones or tablets equipped with a touchscreen, where users can sign using 
a finger or an appropriate pen. Dynamic handwriting recognition usually consists of a 
multi-dimensional temporal analysis, considering multiple variables at a time, e.g. including 
spatial coordinates x(t) and y(t), pressure, azimuth, inclination, pen up/down and velocity.102

Static handwriting recognition 
In this approach (also known as off-line handwriting recognition), the biometric capture 
subject is requested to write a certain text (usually a signature) on paper (which is sub-
sequently digitally scanned) or directly on a digital device so that, in any case, the writ-
ten text is acquired as a digital image. This is then analysed by a system that evaluates 
the shape of the written text to perform biometric recognition.102

2.3.4.

Speaker recognition

Speaker recognition refers to a group of technologies that use information extracted 
from a person’s speech to perform biometric operations such as speaker identification 
and verification.103 It is based on the extraction of acoustic features of speech that dif-
ferentiate individuals. These features convey two kinds of biometric information: phys-
iological properties (anatomical configuration of the vocal apparatus) and behavioural 
traits (speaking style).104

2.3.4.1.

Text-dependent speaker recognition
In this modality, the set of words (or lexicon) used during the biometric acquisition pro-
cess is the same or is a subset of the words used during the enrolment phase. A major 
drawback of text-dependent systems lies in the replay attacks that can be performed 
easily with a simple recording device.105

101 

102 

103 

104 

105 

H. Zhan, C. Xuejie, L. Shiyun and G. Hongyang, “Gait Recognition of Acceleration Sensor for Smart 
Phone Based on Multiple Classifier Fusion”, Mathematical Problems in Engineering, vol. 2019, 2019.

G. A. Khuwaja and M. S. Laghari, “Offline Handwritten Signature Recognition”, World Academy 
of Science, Engineering and Technology, 59, 2011.

J. Markowitz, “Speaker Recognition, Standardization”, Encyclopedia of Biometrics, Springer, Bos-
ton, MA, 2009, pp. 1270-1277.

J. Hennebert, “Speaker Recognition, Overview”, Springer, Boston, 2009, pp. 1262-1270.

S. Z. Li and A. K. Jain, “Text-Dependent”, Encyclopedia of Biometrics, Springer, Boston, MA, 2009, 
pp. 1332-1332.

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Technology ForesighT on BiomeTrics For The FuTure oF Travel2.3.4.2.

2.3.4.3.

Text-independent speaker recognition
In text-independent Speaker recognition, there are no constraints on the words which 
the speakers are allowed to use. Thus, the reference (what is spoken during the train-
ing phase for a Speaker recognition algorithm) and the test (what is uttered in actual 
use) may have completely diﬀerent content, and the recognition process must take this 
phonetic mismatch into account. This modality is most often used for Speaker recogni-
tion, as it requires very little (or no) cooperation by the speaker, thus resulting in more 
flexible solutions than the text-dependent ones.106

Vocal resonance recognition
Vocal resonance is the sound of a person’s voice as it travels through the person’s body. 
To conduct vocal resonance scanning, a microphone may be placed in contact with 
the body in order to record audio samples and then compare them with a model built 
earlier during the training phase. The same methodology may be performed through 
a wearable microphone and reliably distinguish amongst individuals.107

2.3.5.

Other minor behavioural biometrics

2.3.5.1.

2.3.5.2.

As ascertained during this study, other behavioural biometrics exist that are less rele-
vant, than the above-mentioned primary ones, to the fields of border checks and access 
control. For the sake of completeness, these minor behavioural biometrics were includ-
ed in this taxonomical group.

Complex eye movement patterns (eye-tracking)
Class of biometric techniques that use patterns identifiable in human eye movements 
to recognise individuals. The distribution of a set of primitive eye movement features is 
compared with eye movement recordings. This biometric modality is sometimes used 
to ensure robustness against presentation attacks.108 

Oculomotor plant characteristics
Biometrics modality where recognition is performed via the internal non-visible anatom-
ical structure of the eye. To recognise the subject by this method, the anatomical charac-
teristics of the oculomotor plant (comprising the eye globe, its muscles, and the brain’s 
control signals) are estimated. The estimation of the oculomotor plant characteristics 
(OPC) is achieved by analysing the recorded eye movement trajectories via a 2D linear 
homoeomorphic mathematical representation of the oculomotor plant. The derived OPC 
allows recognition via various statistical methods and information fusion techniques.109

106 

107 

108 

K. Tomi and L. Haizhou, “An Overview of Text-Independent Speaker Recognition: from Features 
to Supervectors”, Speech Communication, vol. 52, no. 1, pp. 12-40, 2010.

R. Liu, “Poster: Vocal Resonance as a Passive Biometric”, Proceedings of the 15th Annual Inter-
national Conference on Mobile Systems, Applications, and Services, 2017.

C. D. Holland and O. V. Komogortsev, “Complex Eye Movement Pattern Biometrics: Analyzing 
Fixations and Saccades”, 2013 International Conference on Biometrics (ICB).

109  O. V. Komogortsev, A. Karpov, L. R. Price and C. Aragon, “Biometric authentication via oculo-
motor plant characteristics”, 5th IAPR International Conference on Biometrics (ICB), 2012.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems3.

Technological clusters

As previously mentioned in the Introduction and in Chapter 1, the 57 third-level biomet-
ric technologies and modalities of the taxonomy were grouped into a set of 20 tech-
nological clusters, each representing a level of abstraction defined in order to enable 
Patentometric and Bibliometric Analyses (see Annex III – Patentometric and Bibliometric 
Analyses of Biometric Technologies) and to ensure the usability of the taxonomy in the 
various phases of the Tech Foresight on Biometrics. The definition of the clusters, also 
formulated by applying the Weighted Clusterer software tool, was based on:110
• 

The numerosity of document sets (patents and scientific publications) associated 
with third-level technologies. Patentometric and Bibliometric Analyses mostly rely 
on statistical interpretations of document sets. Therefore, it is preferable to con-
duct these analyses on numerous datasets as, in general, low numerosity implies 
a lower level of statistical consistency and significance. But it is hard to indicate 
the minimum number of patents and scientific publications that a dataset must 
contain in order to be suitable for a proper statistical analysis, since this value 
strongly depends on the ontological features of the technical fields of interest. 
For the present work, 200 was considered the indicative lower limit. Neverthe-
less, in some cases, less numerous data sets were considered to fulfil the need 
to include some specific technological fields within the analyses.
The technological affinity between third-level technologies. This was assessed 
by the Research Team also conducting desk research on the scientific literature. 
Then, every time the documental set related to a biometric technology resulted 
in low numerosity, the technology was grouped together with one or more oth-
er third-level affine technology (if any) to form a cluster.
The relevance of biometric technologies in target applications (border checks, 
biometric recognition and access control). This was assessed by crossmatching 
the stated fields of applications of the selected biometric technologies in patents 
and in the scientific literature with the objectives of the research study.

• 

• 

Table 2 lists the 20 technological clusters that were considered for Patentometric and 
Bibliometric Analyses along with the corresponding biometric technologies belonging to 
the third level of the taxonomy. The table does not include the following technologies:
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 

1.1.4 Techniques for collecting DNA samples
1.2.1 Hand bacteria identification
2.5.5 Encephalographic signals
2.5.6 Acoustic properties of the ear canal
2.7.1 Dental biometrics
2.7.2 Tongueprint recognition
2.7.3 Ear print recognition
2.7.5 Skin texture recognition
2.7.6 Cranial suture scanning
2.7.7 Rugoscopy
2.7.8 MMW and THz wave body imaging

110 

For detailed information on the methodology used to define technological clusters, please refer 
to Annex III – Patentometric and Bibliometric Analyses of Biometric Technologies – Chapter 4.

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r
e
T
s
u
l
C

l
a
C
i
g
o
l
o
n
h
C
e
T

.

3

• 
• 
• 

3.4.3 Vocal resonance recognition
3.5.1 Complex eye movement patterns (eye-tracking)
3.5.2 Oculomotor plant characteristics

These were not considered for Patentometric and Bibliometric Analyses because either 
the associated datasets of patents and scientific publications were too narrow for quan-
titative analysis, or the technologies per se were deemed not sufficiently relevant to 
border-check applications.

Table 2: List of 20 technological clusters and corresponding biometric technologies 
belonging to the third level of the taxonomy, which were considered for conducting Pat-
entometric and Bibliometric Analyses and used in the various phases of the Tech Fore-
sight on Biometrics.

#

1

2

3

4

5

Technological Clusters

Third-level biometric technologies  
included in each technological cluster

DNA biometrics

1.1.1 DNA phenotyping

1.1.2 DNA profiling

1.1.3 DNA sequencing

Infrared face recognition

2.1.1 Thermal Infrared face recognition

2D	face	recognition	in	the	visible	
spectrum

2.1.2 Near-Infrared face recognition

2.1.3 Video-based face recognition

2.1.4 Image-based face recognition

3D	face	recognition

2.1.5 3D face recognition

Infrared friction ridge 
recognition

2.2.1 Thermal friction ridge recognition

2.2.2 Near-Infrared friction ridge recognition

2.2.6 Fingerprint recognition

2.2.7 Palmprint recognition

2.2.8 Footprint recognition

2.2.9 Finger-knuckle-print recognition

6

3D	friction	ridge	recognition

2.2.3 3D friction ridge recognition

7

Contactless friction ridge 
recognition

2.2.6 Fingerprint recognition

2.2.7 Palmprint recognition

2.2.8 Footprint recognition

2.2.9 Finger-knuckle-print recognition

2.2.4 Contactless friction ridge recognition

2.2.6 Fingerprint recognition

2.2.7 Palmprint recognition

2.2.8 Footprint recognition

2.2.9 Finger-knuckle-print recognition

Continued on pg.46

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3

Continued from pg.45

#

8

9

10

11

12

Technological Clusters

Contact-based friction ridge 
recognition

Iris recognition in the NIR 
spectrum

Iris recognition in the visible 
spectrum

Third-level biometric technologies  
included in each technological cluster

2.2.5 Contact-based friction ridge recognition

2.2.6 Fingerprint recognition

2.2.7 Palmprint recognition

2.2.8 Footprint recognition

2.2.9 Finger-knuckle-print recognition

2.3.1 Iris recognition in the NIR spectrum

2.3.2 Iris recognition in the visible spectrum

Iris recognition at a distance

2.3.3 Iris recognition at a distance

Eye vein recognition

2.4.1 Retina recognition

2.4.2 Sclera/episclera recognition

13

Hand vein recognition

2.4.3 Finger vein recognition

2.4.4 Palm vein recognition

2.4.5 Back-of-hand vascular pattern recognition

2.4.6 Wrist vein recognition

14

Heart signal recognition

2.5.1 Heart-rate variability

2.5.2 Electrocardiographic signals

2.5.3 Phonocardiographic signals

2.5.4 Photoplethysmographic signals

15

Hand geometry recognition

2.6.1 Contact-based Hand geometry recognition

2.6.2 Contactless Hand geometry recognition

16

17

Periocular recognition

2.7.4 Periocular recognition

Keystroke recognition

3.1.1 Dynamic Keystroke recognition

3.1.2 Static Keystroke recognition

18

Gait recognition

3.2.1 Gait recognition based on video sensors

3.2.2 Gait recognition based on radar sensors

3.2.3 Gait recognition based on floor sensors

3.2.4 Gait recognition based on wearable sensors

19

Handwriting recognition

3.3.1 Dynamic Handwriting recognition

3.3.2 Static Handwriting recognition

20

Speaker recognition

3.4.1 Text-dependent Speaker recognition

3.4.2 Text-independent Speaker recognition

In the following sections, a brief description of each technological cluster is provided. 
As the reader may notice, these descriptions are often an abridged (and in some case 
combined) version of the definitions provided in Sections 2.1, 2.2 and 2.3 concerning 
the third-level biometric technologies. Such concise descriptions were also meant to 

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Technology ForesighT on BiomeTrics For The FuTure oF Travel 
	
facilitate the quantitative assessment of the 20 technological clusters within the Delphi 
Survey as mentioned in the Introduction. Therefore, if necessary, the readers are referred 
to previous sections for more detailed descriptions and references.

3.1.

DNA biometrics

Amongst the various biometric technologies suitable for the recognition of individu-
als, deoxyribonucleic acid (DNA) provides the most reliable results. This technology is 
intrinsically digital (in terms of biometric data acquisition and processing) and does 
not change during a person’s life or even after their death. DNA biometrics finds eas-
ier applications in forensic science, due to the high invasiveness and low efficiency of 
related modalities. However, other applications in biometric recognition have also been 
proposed. For example, portable human DNA analysers exist that can conduct DNA 
analyses in approximately 25 minutes. Although these systems are still unsuitable for 
applications in the field of access control and border checks, advances are being made 
to expedite the analysis process. This technological cluster includes DNA phenotyping, 
DNA profiling and DNA sequencing.

3.2.

Infrared face recognition

Technologies for the recognition of human faces using infrared (IR) imaging, commonly 
sub-divided into: near-infrared (NIR, 0.78-1.0 µm in wavelength) imaging; short-wave-
length infrared (SWIR, 1-3 µm) imaging; mid-wavelength infrared (MWIR, 3-8 µm) imag-
ing; long-wavelength infrared (LWIR, 8-15 µm) imaging. NIR and SWIR, usually with 
active illumination, are sometimes called “reflected infrared”, while passive MWIR and 
LWIR techniques are sometimes referred to as “thermal infrared” (thermal IR). This clus-
ter includes thermal infrared face recognition and near-infrared face recognition.

3.3.

2D face recognition in the visible spectrum

This technological cluster deals with the automated recognition of individuals through the 
matching of a face, from a digital image or a video frame acquired in the visible spec-
trum of light, against a database of face images or a specified biometric reference image. 
It encompasses video-based face recognition and	image-based	face	recognition.

3.4.

3D face recognition

This cluster includes solutions aimed at recognising an individual by the three-dimen-
sional features of their facial components. Once the three-dimensional geometry of the 
human face is acquired, it is used to extract distinctive features on its surfaces, such as 
the contour of the eye sockets, nose, and chin. Recognition is then based on matching 
the metadata extracted from the 3D shapes of the face. 3D face recognition is claimed 
to have the potential to achieve better accuracy than its 2D counterpart.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems3.5.

Infrared friction ridge recognition

The skin of the palms of the hands and fingers as well as of the soles of the feet and toes 
is distinctly different from that of the rest of the body and is known as friction ridge skin 
in the biometric and forensic communities. This technological cluster includes biomet-
ric recognition modalities (such as Fingerprint recognition, Palmprint recognition, Foot-
print recognition and Finger-knuckle-print recognition) implemented through thermal 
imaging or near-infrared imaging of friction ridge skin.

3.6.

3D friction ridge recognition

This cluster includes the modalities, scanning technologies and image detection sys-
tems capable of acquiring the frictional ridges of one or multiple body parts (i.e. fin-
gers, palms, feet or finger-knuckles) and producing a three-dimensional representa-
tion of them in order to recognise an individual. For example, extracted features from 
3D palmprint data usually include depth and curvature of the palm lines and wrinkles 
on the palm surface.

3.7.

Contactless friction ridge recognition

This cluster includes the modalities in which the friction ridge mark signature of a fin-
ger, palm, foot or finger-knuckle is acquired without direct contact of the relevant body 
part with a sensing surface, mostly employing video or image acquisition of fingerprints, 
palmprints, footprints, and knuckle prints.

3.8.

Contact-based friction ridge recognition

This cluster includes the modalities in which the friction ridge mark signature of a finger, 
palm, foot or finger-knuckle is acquired through the contact of the body part with an 
acquiring surface. For example, contact-based palmprint capture may be performed 
by asking the users to put their hands on a planar surface where their fingers are typ-
ically restricted by pegs.

3.9.

Iris recognition in the NIR spectrum

The iris is a thin, circular structure in the eye that controls the diameter and size of the 
pupils. The iris consists of muscle tissues that cause the pupil to contract and dilate. The 
back surface is covered by a layer of pigmented epithelial tissue, which gives an eye 
its distinctive colour. Iris recognition in the NIR spectrum is the field of biometrics that 
deals with the recognition of individuals through images of the textural features of the 
iris captured using near-infrared illumination. 

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Technology ForesighT on BiomeTrics For The FuTure oF Travel3.10.

Iris recognition in the visible spectrum

This cluster includes iris recognition technologies based on images of the iris captured 
in the visible spectrum of light. This presents many challenging aspects, especially in the 
case of individuals with dark irises (caused by higher melanin pigmentation and collagen 
fibrils) because the unique pattern of the iris is not clearly observable under visible light.

3.11.

Iris recognition at a distance

This cluster refers to technological solutions for capturing iris images metres away from 
the subject and analysing them. With the right detection equipment, Iris recognition 
at a distance might be implemented even for a person walking. This would enhance 
the traveller’s experience at border checks, especially where a large number of people 
moves through a bottleneck (e.g. a border check point), reducing the need for user 
cooperation and achieving low intrusiveness, and thus high acceptance and transparency. 

3.12.

Eye vein recognition

In general, vein (or vascular) pattern recognition uses a light source (usually near-infra-
red light) to acquire images of blood vessels. In the case of Eye vein recognition, scan-
ners typically use harmless low-energy lasers to scan the blood vessels of the eye, and 
users are typically asked to position their eyes in front of the scanner, as eyes must be 
very close to the sensors for the vein patterns to be acquired. This cluster encompass-
es retina recognition and sclera/episclera recognition technologies.

3.13.

Hand vein recognition

This cluster refers to recognition of individuals through images of the complex structure 
of larger blood vessels near the skin of the hand. These may be readily captured from 
the back and the palm of the hand as well as from fingers and wrist, using non-invasive 
and safe imaging techniques (typically in the near-infrared). This vein structure, which 
is mostly invisible to the human eye, forms a pattern of interconnecting lines that is 
different from one individual to another and can be used as a morphological biomet-
ric. This technological cluster includes finger vein recognition, palm vein recognition, 
back-of-hand vascular recognition, and wrist vein recognition.

3.14.

Heart signal recognition

Heart signals belong to the wider group of physiological characteristics of an individ-
ual, i.e. those that do not rely on the morphological characteristics of the human body 
but rather refer to physiological signals that can be acquired and monitored to assess a 
person’s clinical state. This technological cluster includes biometric recognition technol-
ogies based on the detection and acquisition of heart-rate variability (HRV), electro-
cardiographic (ECG) signals, phonocardiographic (PCG) signals, and photoplethys-
mographic (PPG) signals.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems3.15.

Hand geometry recognition

Hand geometry is a widely accepted biometric characteristic that allows the recogni-
tion of individuals from the shape of their hands. Hand geometry readers measure the 
individual’s hand along many dimensions including height, width, deviation and angle, 
and compare those measurements to a reference sample. Although these measure-
ments are not very distinctive amongst people, hand geometry can be very useful for 
biometric recognition. Major advantages of hand geometry systems include: simple 
requirements in imaging resolution (hand features can be extracted from low-resolution 
images); the ability to operate under harsh environmental conditions (hand geometry 
biometrics are immune, for example, to dirt on the hand and other external factors); 
and low data-storage requirements. In addition, hand geometry acquisition and recog-
nition are extremely fast. This technique is widely accepted, and the recognition process 
involves simple data processing. This cluster includes contact-based hand geometry 
recognition and	contactless	hand	geometry	recognition.

3.16.

Periocular recognition

The full region around the eye, including the sclera, eyelids, lashes, brows and skin, is 
known as the periocular region and can be acquired non-intrusively and used as a bio-
metric characteristic. Periocular recognition offers advantages over full-face recognition 
as it is least affected by expression variations, ageing effects and changes due to the 
growth of facial hair. Moreover, this biometric technology may perform better in the 
case of extreme pose changes when only one eye is completely visible.

3.17.

Keystroke recognition

Keystroke dynamics is a behavioural biometric characteristic suitable for the automat-
ed recognition of an individual based on their unique typing rhythm. It refers to the 
detailed timing information and patterns describing how a person types on a digital 
device’s keyboard. Unlike morphological biometric characteristics, such as fingerprint 
or iris, where specialised sensors are necessary to collect the biometric data from the 
captured subject, keystroke dynamics can be detected using off-the-shelf physical com-
puter keyboards or virtual keyboards in smartphones and PDAs. Thus, Keystroke recog-
nition represents a low-cost solution and is easily deployable in a variety of scenarios. 
On the other hand, one of the major drawbacks is that a person’s typing varies sub-
stantially during the day and from day to day, and may be affected by multiple exter-
nal factors. Although other measurements are conceivable, patterns used in keystroke 
dynamics are derived mainly from the two events that make up a keystroke: Key-Down 
and Key-Up. The Key-Down event takes place at the initial depression of a key, and 
the Key-Up occurs at the subsequent release of that key. Various unique features are 
then calculated based on the intra-key and inter-key timing variations between these 
events. This technological cluster includes static keystroke recognition and dynamic 
keystroke recognition.

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Technology ForesighT on BiomeTrics For The FuTure oF Travel3.18.

Gait recognition

Gait is a type of behavioural biometric characteristic used to recognise individuals by 
their walking style (or manner) and pace. Gait has several advantages compared to other 
biometric characteristics: in most modalities, Gait recognition is non-intrusive, does not 
require cooperation from the individual and can function at moderate distances from 
the subject. This technological cluster is formed by Gait recognition technologies based 
on video sensors, radar sensors, floor sensors and wearable sensors. 

3.19.

Handwriting recognition

Handwriting recognition is the process of recognising the author of a text from their 
handwriting style, and includes (and mostly refers to) signature recognition. Handwriting 
recognition can be applied to a generic text or to a specific predefined text (usually a 
signature) and implemented according to two main modalities, included in this techno-
logical cluster: dynamic handwriting recognition and static handwriting recognition.

3.20.

Speaker recognition

Speaker recognition refers to a group of technologies that use information extracted 
from a person’s speech to perform biometric operations such as speaker identification 
and verification. It is based on the extraction of acoustic features of speech that differ-
entiate individuals. These features convey two kinds of biometric information: physio-
logical properties (anatomical configuration of the vocal apparatus) and behavioural 
traits (speaking style). This technological cluster includes text-dependent speaker rec-
ognition and text-independent speaker recognition.

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems4.

Conclusions

The work presented in Part 1 – Taxonomy of biometric technologies focused on a sys-
tematic taxonomical categorisation, conducted on a set of identified biometric technol-
ogies relevant for border checks. The output of the process consisted of:
a) 

a taxonomy of biometric technologies (whose tree-style graphical represen-
tation is shown in Figure 2), developed to establish a common understanding of 
the biometrics technological domain and to create a reference document that 
could be used in research and innovation activities in the future.
the detailed description of the 57 biometric technologies and modalities.

b) 

The taxonomy was organised in a three-level hierarchical structure with a progressively increas-
ing degree of complexity: first-level biometrics macro-areas; second-level biometric tech-
nological fields; third-level biometric recognition or acquisition technologies and modalities.

Morphological biometrics is the most copious group of biometric technologies pertinent 
to the investigated application domains of border checks, biometric recognition and access 
control. Meanwhile, biomolecular biometrics, mostly formed by DNA-related technolo-
gies, turned out to be the narrowest macro-area. In fact, even though the DNA-related 
biometric technologies provide the most reliable results, they are still characterised by 
highly invasive and low-efficiency sample capture and processing. Behavioural biomet-
rics was also found to be significantly less represented than morphological biometrics. 
This is possibly due to the high levels of reliability and efficiency required in the investi-
gated domains, which may be difficult for behavioural biometric technologies to achieve. 

Patents and scientific literature proved to be suitable sources of information for taxonomy 
development. Moreover, patents allowed the Research Team to retrieve a higher num-
ber of less-known technologies than with the scientific literature. Examples of these are:
• 
• 
• 
• 
• 
• 
• 
• 
• 

Hand bacteria identification,
Encephalographic signals,
Acoustic properties of the ear canal,
Dental biometrics,
Tongueprint recognition,
Cranial suture scanning,
Rugoscopy,
Complex eye movement patterns (eye-tracking),
Oculomotor plant characteristics.

Given the small amount of related documentation, these technologies were not con-
sidered in Patentometric and Bibliometric Analyses. Nonetheless, their inclusion within 
the proposed taxonomy is useful as they may be considered for research and innova-
tion activities in the future.

The scientific literature and the knowledge of the Research Team Experts represent-
ed the most reliable sources of information to validate and to provide a description of 
the technologies to be included in the taxonomy. Patents are, in fact, generally written 
to enlarge the scope of application of the reported inventions, thus often resulting in 
abstract and non-specific descriptions.

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Technology ForesighT on BiomeTrics For The FuTure oF TravelPart 2 – Taxonomy of 
biometrics-enabled 
technological systems

5. 

Methodology

The taxonomy of biometrics-enabled technological systems was developed to provide 
an indicative reference, definition and classification guide to the systems and sub-sys-
tems that will make use of, or rely on, the various individual biometric technologies 
reported in Part 1 – Taxonomy of biometric technologies, within the context of new and 
emerging methodologies and processes worldwide for border checks and management.

To provide information within the Tech Foresight on Biometrics about the possible future 
use of biometric technologies within the context of border checks, the Research Team 
considered the system aspects and use cases to which these various biometric technol-
ogies may be applied, as well as how the individual traveller would interact with them.

We analysed the end-to-end journey of a traveller as they encounter various techno-
logical systems along their way, from first booking the travel to arrival, passing control 
and being accepted into their final destination point. Sources such as the vision pre-
sented in IATA NEXTT – Building the journey of the future,111 the World Economic Forum’s112 
project on Shaping the Future of Security in Travel113 and the World Travel and Tourism 
Council’s114 (WTTC) publicly available information on the future of travel provided valu-
able background information. The 2021-26 vision and strategy report115 of the US Cus-
toms and Border Protection Agency116 (CBP) also provided insight into CBP’s thinking 
about how to facilitate travel while reducing the friction of interaction with the traveller, 
yet maintaining and enhancing the prerequisites of national security.

The taxonomy of biometrics-enabled technological systems took into consideration the 
Research Team Experts’ knowledge regarding existing and projected systems worldwide, 
along with EU-funded projects such as PROTECT117 and D4FLY,118 which are part of the 
EU’s Horizon 2020 research and innovation funding programme focused on research-
ing and evaluating the security and convenience aspects of potential future traveller 
management systems.

111 

112 

113 

114 

115 

116 

117 

118 

https://www.nextt.aero/en/

https://www.weforum.org/

https://www.weforum.org/videos/shaping-the-future-of-security-in-travel

https://wttc.org/

https://www.cbp.gov/newsroom/national-media-release/cbp-releases-2021-2026-strategy

https://www.cbp.gov/

http://projectprotect.eu/

https://d4fly.eu/

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTemsy
g
o
l
o
d
o
h
T
e
m

.

5

The taxonomy first identified, at the top level, groups of systems that may use biometric 
technologies a traveller would knowingly or unknowingly interact with along their jour-
ney. For example, the top level includes the group of large-scale IT systems that form 
the all-important data analysis and decision-making back-end infrastructure that ulti-
mately provides the informative distillation of data on which a border guard authority, 
or in the case of automation an ABC system, permits or denies the traveller passage 
across a state border.

Each of these top-level groups was then sub-divided into second-level categories that 
encompass all the possible types of instantiations of a particular group. In each category, 
there is a specific element of biometric data acquisition and evaluation that relates to 
an aspect of recognising travellers and ascertaining their entitlement to travel.

The taxonomy should be read in conjunction with the Technical Guide for Border Checks 
on EES-related equipment119 recently published by Frontex. This technical guide provides 
an overview of the minimum technical requirements for Manual Border Control (MBC) 
Systems; Self-Service Systems (SSS); e-Gates & Automated Border Control (ABC) Systems; 
and Mobile Systems. One of its primary aims is the technical support of Member States’ 
procurements related to the implementation of the EU Entry/Exit System. The technical 
guide does make significant reference to MBC Systems, which were not explicitly cov-
ered in the taxonomy. However, current MBC Systems and their likely evolution in the 
immediate future use many of the technological sub-systems detailed in the taxonomy.

The proposed taxonomy of biometrics-enabled technological systems focuses on solu-
tions that can be used to design and implement a preferred service architecture over 
a period in which seamless and automated border checks are expected to become the 
norm rather than the exception.

119 

European Border and Coast Guard Agency, “Technical Guide for Border Checks on Entry/Exit 
Systems (EES) related equipment”, Publications Office of the European Union, Luxembourg, 
2021.

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Technology ForesighT on BiomeTrics For The FuTure oF Travel 
6. 

Taxonomy description

The final tree-style representation of the proposed taxonomy of biometrics-enabled 
technological systems is shown in Figure 3. The following sections contain a descrip-
tion for each element of the tree.

Figure 3: Graphic representation of the taxonomy of biometrics-enabled technological systems.

1

self-service 
systems

1.1.    E-gates

1.3.    Kiosks

1.2.    Corridors

1.4.    Remote on-boarding systems

2

identity document 
readers and  
verification systems

2.1.    Integrated document readers/scanners

2.2.    Desktop document readers/scanners

2.3.    Forensic document readers/scanners

2.4.    Handheld document readers/scanners

2.5.    Holographic document readers/scanners

2.6.    Document analysis software

3

full-Body  
scanning  
systems

3.1.    Scanners using mm or THz waves

3.3.    Systems using quantum sensors

3.5.    Camera-based scanning systems

3.2.    Scanners using radar wavebands

3.4.    Holographic scanning systems

4

systems Based on  
personal devices

4.1.    Smartphone device biometrics

4.3.    Wearable biometric technologies

4.2.    Handheld device biometrics

4.4.    Microchip biometric implants

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems5

movaBle  
systems

5.1.    Mobile (non-autonomous) checkpoints

5.2.    Semi-autonomous robotic systems

5.3.    Autonomous robotic systems

5.4.    Systems for the seamless recognition of persons in transport

6

large-scale it systems 
that deploy Biometriic 
comparison components

6.1.    Traveller information and entry-exit systems

6.2.    Visa and travel authorisation systems

6.3.    Biometric central databases for border control and law enforcement

7

virtual traveller 
identity schemes for 
Biometrics-enaBled 
technological systems

7.1.    ICAO “Digital Travel Credential” (DTC)

7.2.    IATA “Travel Pass”

7.3.    WEF “Known Traveller Digital Identity”

7.4.    Proprietary schemes

7.5.    Digital Trust Frameworks

Please note that the references given below include publicly available materials provided 
by several commercial companies. References do not imply any commercial interest by 
EU entities nor make any recommendation. The reference citations are simply intended 
to assist with the illustration or description of a specific use case or examples of prod-
ucts and services useful for the construction of the taxonomy.

6.1. 

Self-service systems 

Systems with which a traveller can interact on their own without requiring direct super-
vision or interaction with another person or official.

6.1.1. 

E-gates

This category includes Automated Border Control (ABC) and Assisted Border Control gates 
that are connected to a system with sensors that automatically or semi-automatically 

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Technology ForesighT on BiomeTrics For The FuTure oF Travelrecognises the traveller using protocols and checks approved for the purpose of a state’s 
immigration policy.120, 121, 122, 123

6.1.2.

Corridors

This category refers to multi-sensor deployments designed to operate within corri-
dors/spaces where travellers walk/ride, for the purpose of guiding and performing the 
biometric acquisition and recognition of those individuals within the corridor or space.

There are possible sub-categories:124, 125, 126 
• 
• 
• 
• 
• 

6.1.2.1 single person, single lane, 
6.1.2.2 single person, multi-lane, 
6.1.2.3 multi-person, single lane, 
6.1.2.4 multi-person, multi-lane, 
6.1.2.5 defined space, without lanes or files. 

6.1.3.

Kiosks

This category refers to self-service systems that may be deployed in various locations 
within an international travel terminal, e.g. check-in, pre-entry, pre-exit, and transit. Kiosks 
provide system-connected functions such as document and ticket checking, issuing 
boarding passes, baggage check-in, verification of API/PNR manifests, and verification 
back to the system that a traveller is within the terminal complex.127

6.1.4.

Remote on-boarding systems 

This category refers to systems128 in which a personal device (e.g. a device belonging to 
Category 6.4 in this taxonomy) is used to collect initial data, potentially including bio-
metrics, for the purpose of verifying a traveller, their journey and their rights to travel. 

120 

121 

122 

123 

See for example https://www.cbp.gov/newsroom/national-media-release/cbp-enhances-bio-
metrics-non-us-travelers-entering-and-exiting-united

J. Sanchez del Rio, “Automated border control e-gates and facial recognition systems”, Elsevier 
Computers and Security, vol. 62, pp. 49-72, 2016.

C. Morosan, “Information Disclosure to Biometric E-gates: The Roles of Perceived Security, Ben-
efits, and Emotions”, Journal of Travel Research, vol. 57, no. 5, pp. 644-657, 2017.

See also Section 3.3 in: European Border and Coast Guard Agency, “Technical Guide for Border 
Checks on Entry/Exit Systems (EES) related equipment”, Publications Office of the European 
Union, Luxembourg, 2021.

See also https://www.futureairport.com/contractors/airport-security/kaba-gallenschuetz-gmbh/

124 
125  O. Hamdoun, “Person re-identification in multi-camera system by signature based on interest 
point descriptors collected on short video sequences”, 2nd ACM/IEEE International Conference 
on Distributed Smart Cameras (ICDSC-08), Stanford, Palo Alto, 2008.

126  M. M. Kalayeh, E. Basaran, M. Gökmen, M. E. Kamasak and M. Shah, “Human Semantic Parsing 
for Person Re-Identification”, Proc IEEE Conference on Computer Vision and Pattern Recogni-
tion (CVPR), 2018.

127 

128 

See for example https://www.idemia.com/morphomaestro-kiosks-and-gates-supervision

Two of many examples of typical commercial offerings are given in: 
https://www.idemia.com/news/future-remote-identity-verification-and-digital-onboard-
ing-2020-05-14 
https://www.thalesgroup.com/en/markets/digital-identity-and-security/government/identi-
ty-verification

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTemsFurther functions might include pre-populating traveller information (e.g. API/PNR) 
into the system that will eventually verify the traveller throughout their journey, from 
departure to destination arrival and immigration check. Analogous to KYC (Know Your 
Customer) systems used for on-boarding in online and digital banking systems.

6.2.

Identity document readers  
and verification sub-systems 

Equipment and software for reading and verifying the data contained in eMRTDs and 
managing its transfer to other sub-systems, including the large-scale IT systems of 
Category 6.6.

6.2.1.

Integrated document readers/scanners

These systems typically include a video spectral comparator equipped with multiple illu-
mination sources (usually Visible, NIR, UV) for scanning and verifying high resolution 
images of government-issued documents such as passports, visas, and ID cards includ-
ing eMRTDs.129 They also usually include ICAO-compliant chip reading and verification 
functions, e.g. for reading biometric and biographic data (accessing stored biometric 
data from a chip or other source) and verification (confirming a biometric claim through 
biometric comparison130). Deployed in kiosks and e-gates.131

6.2.2.

Desktop document readers/scanners

Similar to 6.2.1, but with a different form-factor more suitable for stand-alone use at 
BCP desks for first-line inspection.132

6.2.3.

Forensic document readers/scanners

Scanners that may be deployed as second- or third-line document inspection devices, 
where checks conducted using a Category 6.2.1 or 6.2.2 device fail or flag a suspicious 
document. These instruments offer higher resolution imaging, special-purpose image 
extraction capabilities and the ability to visualise holographic/diffractive security fea-
tures. They may also include ICAO-compliant chip reading and verification.133

129 

130 

131 

See for example: 
https://regulaforensics.com/en/products/ 
https://www.thalesgroup.com/en/markets/digital-identity-and-security/government/docu-
ment-readers/oem-kiosk-readers 
https://www.desko.com/en/products/overview/desko-penta-scanner/

ISO, International Standard ISO/IEC 2382-37:2017 — Information technology — Vocabulary — 
Part 37: Biometrics, 2017.

See Paragraph 2.2.2 in: European Border and Coast Guard Agency, “Technical Guide for Border 
Checks on Entry/Exit Systems (EES) related equipment”, Publications Office of the European 
Union, Luxembourg, 2021.

132  One example of commercial offering: https://www.desko.com/en/products/overview/des-

ko-penta-scanner/

133 

See for example this commercial offering: https://regulaforensics.com/en/products/special_
equipment/8850/

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Technology ForesighT on BiomeTrics For The FuTure oF Travel6.2.4.

Handheld document readers/scanners

Handheld devices with a dedicated, multi-illumination document reader/scanner134 func-
tion, providing similar functionality to devices described in Categories 6.2.1 or 6.2.2. 

6.2.5.

Holographic document readers/scanners

Specialised document readers, or adjunct devices to document readers, which scan doc-
uments’ holographic/diffractive optical security features.135 So far, equipment that truly 
separates imagery from holographic or diffractive optically variable security features 
appears to be limited to equipment specifically aimed at forensic document inspection 
tasks, for example136 Category 6.2.3.

6.2.6.

Document analysis software 

Category that refers to the dedicated software that works with data fed into the underly-
ing border management IT system for the purpose of verifying data and images acquired 
through identification document readers of the type described in Categories 6.2.1–6.2.5 
of this taxonomical group. This software may include biometric data extraction (e.g. 
photos from the chip) and potentially biometric matching functions.137

134 

135 

See an example of commercial offering at: https://regulaforensics.com/en/products/machine_
verification/7308/

See https://www.biometricupdate.com/202012/new-id-document-scanning-technologies-ad-
dress-security-privacy-concerns

136  One example of commercial offering may be found at: http://www.fosterfreeman.com/prod-

uct/qde-products/580-vsc8000.html

137 

See an example of commercial offering at: https://regulaforensics.com/en/products/advanced_
verification/frontline-documents-system/

59 / 68

Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems6.3.

Full-body scanning systems

Equipment that acquires images for biometric purposes from a traveller’s whole body, 
using techniques described in the following paragraphs. The subsequent analysis of 
the acquired images may occur within the scanner equipment itself or may be passed 
to another sub-system.

6.3.1.

Scanners using mm or THz waves

This category refers to full-body imaging systems designed to use stimulus and detec-
tion devices operating in the 30 GHz to 300 GHz frequency bands (10 mm-1 mm wave-
length or “mm wave”) or in the 300 GHz to 10 THz frequency bands (1 mm to 30 um 
wavelength or “THz wave”) of the electromagnetic spectrum. Such systems are currently 
used in security screening for concealed item detection but have the technical poten-
tial to be used for biometrics.138, 139, 140

Note that mm-wave radars (see 6.3.2) operating at ~30 GHz could also be considered 
in this category, as their spectral band overlaps with the lowest frequency considered 
in this category.

6.3.2.

Scanners using radar wavebands

This category refers to full-body imaging systems designed to use stimulus and detec-
tion devices operating in the parts of the electromagnetic spectrum associated with 
radars141 (wavelength in the range of 30 cm to 10 mm or frequency from 1 to 30 GHz).140 

6.3.3.

Systems using quantum sensors

A quantum sensor is a quantum device (usually referring to quantised energy levels 
and the exploitation of quantum mechanical phenomena) that responds to a stimulus 
using quantum coherence to measure a physical quantity,142 or using entanglement to 
improve measurements beyond what can be achieved with classical sensors.143 Making 
quantum measurements of human body attributes for application in biometrics is at a 
very early stage of research.

138 

139 

J. Laviada, A. Arboleya-Arboleya and F. Las Heras, “Multistatic Millimeter-Wave Imaging by Mul-
tiview Portable Camera”, IEEE Access, vol. 5, pp. 19259-19268, 2017.

D. McMakin, “New Improvements to Millimeter-Wave Body Scanners”, 3D BODYTECH 2017 – 
8th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Montreal, Canada, 
2017.

140  D. McMakin, D. M. Sheen, T. E. Hall, M. O. Kennedy and H. P. Foote, “Biometric identification 
using holographic radar imaging techniques”, Defense and Security Symposium, Orlando, Flor-
ida, United States, 2007.

141 

142 

143 

Radar wave-band definitions can be found at: https://www.radartutorial.eu/07.waves/Waves%20
and%20Frequency%20Ranges.en.html

See for example https://www.quantumsensors.org/technology/sensing-the-brain

T. Bowler, “How quantum sensing is changing the way we see the world”, BBC News, 8 March 
2019.

60 / 68

Technology ForesighT on BiomeTrics For The FuTure oF Travel6.3.4.

Holographic scanning systems

Category of systems that refers to a real-time optical recording technique in which 
holographic information about a three-dimensional (3D) object (in this case all or part 
of a human body) is acquired using a single two-dimensional (2D) active optical scan.144

6.3.5.

Camera-based scanning systems

This  category  encompasses  camera-based  data  acquisition  systems  intended  for 
multi-purpose sensing, e.g. face, gait, and somatotype. They may typically deploy an 
array of cameras, whose spatial geometry may be important to the biometric acquisi-
tion process, and require specific image analytic techniques for decoding the biometric 
data so acquired, e.g. photogrammetry. The camera array may include devices special-
ised for different wavelengths, e.g. Visible, NIR, SWIR, MWIR, and LWIR.145

144 

145 

T. Chung Poon, “Optical Scanning Holography – A Review of Recent Progress”, Journal of the 
Optical Society of Korea, vol. 13, no. 4, pp. 406-415, 2009.

A. Jirafe, M. Jibhe and V. Satpute, “Camera Handoff for Multi-camera Surveillance”, Applications 
of Advanced Computing in Systems. Algorithms for Intelligent Systems, Springer Singapore, 
2021.

61 / 68

Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems6.4.

Systems based on personal devices

Personal devices such as smartphones and smartwatches contain many sensors from 
which biometric data may be acquired and analysed, either within the device itself where 
appropriate or by transmitting the data via a suitable route to another sub-system.

6.4.1.

Smartphone device biometrics

This category comprises system use cases where biometric data is acquired and aggre-
gated using the built-in sensors of a smartphone, and where such data is either com-
pletely or partially processed locally on the device and includes the associated data 
analysis and communication methodology.146, 147

6.4.2.

Handheld device biometrics

This category comprises system use cases similar to 6.4.1 but where a dedicated hand-
held device (which may or may not be based on commercial smartphone technology) 
is used.148, 149 Typically, such use cases refer to the trusted usage of the device by an 
official and may circumvent the issue of trust in the data source. 

6.4.3.

Wearable biometric technologies

This category refers to biometric sensors embedded within devices or clothing worn 
by a user and from which biometric data may be acquired, aggregated, analysed and 
communicated to an external system.

6.4.4.

Microchip biometric implants

This category encompasses chip-based devices embedding biometric sensors, implanted 
in a user’s body for the purpose of acquiring, aggregating, analysing, and communi-
cating biometric data to an external system.150 Such biometric implants would typically 
comprise three components: an integrated circuit with embedded sensors for acquiring, 
pre-processing, and possibly temporarily storing biometric information, an antenna for 
receiving and transmitting signals between that circuit and an external reader/proces-
sor, and a means for supplying power to the whole assembly.151

146 

147 

148 

149 

150 

R. Blanco-Gonzalo and R. Sanchez-Reillo, “Biometrics on Mobile Devices”, Encyclopedia of Bio-
metrics, Springer, Boston, 2014, pp. 1-8.

See also Digital Mobile equipment and application for travellers within Paragraph 1.5.3 in: Euro-
pean Border and Coast Guard Agency, “Technical Guide for Border Checks on Entry/Exit Sys-
tems (EES) related equipment”, Publications Office of the European Union, Luxembourg, 2021.

An example of a commercial offering: https://news.northropgrumman.com/file?fid=57d963042c-
fac27bff90edeb

Another example of a commercial offering: https://www.irisid.com/productssolutions/hard-
wareproducts/icam-m300/

P. Rotter, B. Daskala and R. Compañó, “RFID implants: Opportunities and challenges for iden-
tifying people”, Technology and Society Magazine, vol. 27, pp. 24-32, 2008.

151 

For example, a commercial offering may be found at: https://www.vivokey.com/ecosystem

62 / 68

Technology ForesighT on BiomeTrics For The FuTure oF Travel6.5.

Movable systems

Systems for collecting data about an individual traveller or user but comprising equip-
ment that can be moved either under human control or autonomously to interact with 
the user (rather than the user deliberately moving into place).

6.5.1.

Mobile (non-autonomous) checkpoints

These systems represent a variation of the kiosk theme of Category 6.1.3 but refer to 
devices that can be moved up to a user, either under supervised, remote control or 
self-service operation.152

6.5.2.

Semi-autonomous robotic systems

Devices or equipment that carry biometric sensors (e.g. cameras), and that move under 
remote or supervised control to detect and recognise persons within a controlled area; 
for example drones which are remotely controlled by a human operator to locate the 
subject of interest.153 Data acquisition and analysis tasks will be largely or entirely auto-
mated.

6.5.3.

Autonomous robotic systems

Devices or equipment that carry biometric sensors (e.g. cameras) and that move auton-
omously (i.e. without explicit human control) to detect and recognise persons within an 
area; for example drones with autonomous “seek and detect” capability.154

6.5.4.

Systems for the seamless recognition of persons in transport

Systems capable of seamless biometric acquisition and recognition of persons staying 
inside various vehicles or modes of transport, i.e. in trucks, cars, coaches, trains, and 
ships.155

152 

In the EU EES context, such mobile checkpoints are covered in Paragraph 1.5.3 in: European 
Border and Coast Guard Agency, “Technical Guide for Border Checks on Entry/Exit Systems 
(EES) related equipment”, Publications Office of the European Union, Luxembourg, 2021.
153  H.-J. Hsu and K.-T. Chen, “Face Recognition on Drones: Issues and Limitations”, DroNet '15: Pro-
ceedings of the First Workshop on Micro Aerial Vehicle Networks, Systems, and Applications 
for Civilian Use, Florence, Italy, 2015.

154 

Y. Kortli, M. Jridi, A. Al Falou and M. Atri, “Face Recognition Systems: A Survey”, Sensors, vol. 20, 
no. 2, p. 342, 2020.

155  M. Ruby, “The Mertens Unrolled Network (MU-Net): a high dynamic range fusion neural net-

work for through the windshield driver recognition”, DeepAI, 2020.

63 / 68

Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems6.6.

Large-scale IT systems that deploy biometric 
comparison components

These are the back-end systems and infrastructure that analyse and evaluate the data 
collected from individual travellers and users as they interact with the data collection 
front-end systems.

6.6.1.

Traveller information and entry-exit registration systems

Large-scale IT systems capable of electronically registering the time and place of transit 
(entry and exit) of travellers through a border crossing point and calculating the dura-
tion of their authorised stay. These systems usually deploy document readers as well as 
biometric acquisition and recognition systems and have access to central or federated 
biometrics data repositories that are used for biometric comparison and identification 
in the context of border checks (at exit or entry). Typically, biometric samples, along 
with other data associated with the individual in question, are submitted to the system 
and a pass/fail result for verification or an identification result is returned, often with 
a confidence score, and with watchlist detection which may be via associated queries 
to other databases that are not an integral part of the Entry-Exit System itself. Exam-
ples of this are the EU Entry/Exit System156 (EES) and the Biometric Facial Comparison 
system used within the US CBP157 proposed Biometric Entry/Exit Program.158, 159, 160, 161, 162

EU large-scale IT systems that use biometrics and may be queried during part of an 
entry/exit process include163 the Entry/Exit System (EES), the Schengen Information Sys-
tem (SIS), the Visa Information System (VIS), the European Asylum Dactyloscopy Data-
base (Eurodac) and the European Criminal Records Information System – Third Coun-
try Nationals (ECRIS-TCN).

6.6.2.

Visa and travel authorisation systems

Large-scale IT systems that underpin visa policies and electronic visa waiver schemes to 
facilitate border checks as well as pre-travel authorisation systems to verify whether a 
traveller meets entry requirements before travelling to a certain destination. In the EU, 

156 

157 

158 

159 

160 

161 

162 

163 

https://ec.europa.eu/home-affairs/what-we-do/policies/borders-and-visas/smart-borders/ees_en

https://biometrics.cbp.gov/

G. Glouftsios, “Governing border security infrastructures: Maintaining large-scale information 
systems“, Security Dialogue, 2020.

A. Helala, H. Muhammad, A. Hatim and A. Mansour, “A Large-Scale Study of Fingerprint Match-
ing Systems for Sensor Interoperability Problem”, Sensors, vol. 18, no. 4, p. 1008, 2018.

S. Hemalatha, “A systematic review on Fingerprint based Biometric Authentication System”, 
2020 International Conference on Emerging Trends in Information Technology and Engineer-
ing (IC-ETITE), 2020.

European Agency for the operational management of large-scale IT systems in the area of 
freedom, Biometrics in Large-Scale IT. Recent trends, current performance capabilities, recom-
mendations for the near future, 2015.

Congressional Research Service (CRS), Biometric Entry-Exit System: Legislative History and Sta-
tus, 2020.

A description of such large-scale IT systems may be found at: https://www.eulisa.europa.eu/
Activities/Large-Scale-It-Systems

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Technology ForesighT on BiomeTrics For The FuTure oF Travelsystems that perform these functions include the Visa Information System (VIS)164 and 
the European Travel Information and Authorisation System (ETIAS).165 Examples outside 
the EU are ESTA166 (in the US), the eTA167 (in Canada), and the NZeTA168 (in New Zea-
land). These systems may have potential to collect biometric data, e.g. by reading the 
biometric information stored in an e-passport chip where this is used to facilitate the 
traveller’s enrolment into the visa or travel authorisation system.

6.6.3.

Biometric central databases for border control and law enforcement

Databases of biometric data that may be accessed as part of a border check on an indi-
vidual, for example, to ascertain whether that person is a known criminal, terrorist or on 
a wanted watchlist, or to deal with asylum seekers and irregular migrants. Examples of 
such databases in the EU are the Schengen Information System (SIS II)169, the European 
Asylum Dactyloscopy Database (Eurodac)170 and the European Criminal Records Infor-
mation System – Third Country Nationals (ECRIS-TCN)171 managed by eu-LISA,172 and 
the Europol Information System (EIS).173

164 

165 

166 

167 

168 

169 

170 

171 

172 

173 

https://www.eulisa.europa.eu/Activities/Large-Scale-It-Systems/Vis

https://www.eulisa.europa.eu/Activities/Large-Scale-It-Systems/Etias

https://www.cbp.gov/travel/international-visitors/esta#

https://www.canada.ca/en/immigration-refugees-citizenship/services/visit-canada/eta.html

https://www.immigration.govt.nz/new-zealand-visas/apply-for-a-visa/about-visa/nzeta

https://www.eulisa.europa.eu/Activities/Large-Scale-It-Systems/Sis-Ii

https://www.eulisa.europa.eu/Activities/Large-Scale-It-Systems/Eurodac

https://www.eulisa.europa.eu/Activities/Large-Scale-It-Systems/Ecris-Tcn

https://europa.eu/european-union/about-eu/agencies/eu-lisa_en#overview

https://www.europol.europa.eu/operations-services-and-innovation/services-support/infor-
mation-exchange/europol-information-system

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Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems6.7.

Virtual traveller identification schemes for 
biometrics-enabled technological systems

Systems and processes aimed at facilitating the journey of an individual traveller to ulti-
mately make it seamless, from initial journey booking, through the verification of enti-
tlement, to arrival and acceptance into their destination.

6.7.1.

ICAO “Digital Travel Credential” (DTC)

Virtual traveller identity scheme conforming to the specifications defined by the ICAO, 
including its issuance and verification protocols.174

6.7.2.

IATA “Travel Pass”

Virtual traveller identity scheme conforming to specifications defined by IATA, including 
its issuance and verification protocols.175 Travel Pass is part of the IATA NEXTT176 initiative 
and uses the OneID177 traveller digital identity concept developed by IATA.

6.7.3.

WEF “Known Traveller Digital Identity” (KTDI)

Virtual traveller identity scheme conforming to specifications defined by the World 
Economic Forum (WEF), including its issuance and verification protocols. It uses open-
source techniques described in the Commons project.178

6.7.4.

Proprietary schemes

Other commercial proprietary schemes for preparing, issuing, and verifying virtual trav-
eller identity credentials, which may or may not conform to or implement techniques 
described in Categories 6.7.1, 6.7.2 or 6.7.3 and which may include biometric content, 
as proposed by various companies.179

174 

175 

176 

177 

178 

179 

ICAO Technical Advisory Group on the Traveller Identification Programme (TAG/TRIP), Subgroup 
of the New Technologies Working Group (NTWG), “Guiding Core Principles for the Develop-
ment of Digital Travel Credential (DTC)”, 2020.

Find more at: https://www.iata.org/en/programs/passenger/travel-pass/

https://www.nextt.aero/en/

https://www.iata.org/en/programs/passenger/one-id/

https://ktdi.org/
https://www.weforum.org/agenda/2020/08/covid19-coronavirus-travel-data-privacy-com-
monpass/

Some examples: 
Zamna (https://zamna.com/) 
Travizory (https://www.travizory.com/) 
Daon (https://www.daon.com/verifly-seamless-travel/verifly-solution) 
HID (https://www.hidglobal.com/citizen-identity) 
Idemia (https://www.idemia.com/easy-and-legitimate-border-crossing) 
SITA (https://www.sita.aero/pressroom/news-releases/sita-steps-up-smart-border-solutions-
to-support-new-regulations-for-entry-and-exit-to-the-eu-schengen-zone/)

66 / 68

Technology ForesighT on BiomeTrics For The FuTure oF Travel6.7.5.

Digital Trust Frameworks

This category includes systems and protocols that typically deploy privacy-enhancing 
techniques to protect Personally Identifiable Information (PII), including biometric data, 
while facilitating the querying and verification of such PII data.180 They are an important 
mechanism to be considered for the implementation of virtual traveller identity schemes.

180 

For example, see: 
https://www.gov.uk/government/publications/the-uk-digital-identity-and-attri-
butes-trust-framework/
https://www.dta.gov.au/our-projects/digital-identity/trusted-digital-identity-framework
https://digital-strategy.ec.europa.eu/en/library/building-trusted-and-secure-european-digi-
tal-identity-brochure

67 / 68

Taxonomy of BiomeTric Technologies and BiomeTrics-enaBled Technological sysTems7.

Conclusions

It is hoped that the proposed taxonomy of biometrics-enabled technological systems 
provides an informative starting point from which forward-thinking into the deployment 
of biometrics, as well as the systems and sub-systems that use the biometric data, may 
be further developed for a more efficient management of border control.

2. 

Three key aspects are suggested to be considered when implementing future biomet-
rics-enabled technological systems:
1.  Ways in which individual travellers, including those with differing levels of ability 
and understanding, are likely to interact with and react to the systems and pro-
cesses they encounter or are presented with.
The progress of technology and its capabilities within the various categories iden-
tified in the taxonomy, taking into account how the sub-systems might interact 
with and complement each other. 
The progress of legislation related to the employment and ethics of biometrics 
as well as the balance between protecting fundamental rights and maintaining 
the security and integrity of external borders.

3. 

Furthermore, as anticipated in the introduction, this taxonomy played an essential role 
within the Tech Foresight on Biometrics in guiding the development of technological 
roadmaps, where a specific layer was included to envisage future products and sys-
tems that might emerge exploiting the biometric technologies included in the clusters.

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Technology ForesighT on BiomeTrics For The FuTure oF TravelIII / IV

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T +48 22 205 95 00 
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