Artificial intelligence and large-scale IT systems : opportunities and challenges : 4-11-18 and 25 November 2021 online event.
Date: 2022-03-30
Institutions: European Union Agency for the Operational Management of Large-Scale IT Systems in the Area of Freedom, Security and Justice
Subjects: artificial intelligence, big data, biometrics, computer system, digital technology, information security, new technology, protection of privacy

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Industry Roundtable
November 2021 - Report

Artificial Intelligence and 
Large-Scale IT Systems: 
Opportunities and 
Challenges

4 - 11 - 18 and 25 November 2021
Online Event

2

This report is public. Reproduction is authorised, 
except for commercial purposes, provided that the 
source is acknowledged.

Catalogue number: EL-AN-21-002-EN-N 
ISBN 978-92-95217-97-3
ISSN: 2600-2728
DOI:10.2857/870875

eulisa.europa.eu

© European Union Agency for the Operational 
Management of Large-Scale IT Systems in the Area 
of Freedom, Security and Justice (eu-LISA), 2022

Industry Roundtable November 2021 Reporteulisaroundtable.euTable of Contents

3

Introduction ........................................................................................................................................................................ 4

Session I - 4 November 2021. Artificial Intelligence for Advanced Data Analytics and Forecasting 
to Support Business Processes in the Area of Internal Security

Opening Remarks by Krum Garkov, Executive Director of eu-LISA ................................................................................. 6

AI for Law Enforcement: The Slovenian Experience ........................................................................................................ 7

Project Presentation: ITFLOWS – IT Tools and Methods for Managing Migration Flows.............................................8

Q&A Session ....................................................................................................................................................................... 9

Industry Presentations ....................................................................................................................................................10

Fujitsu Technology Solution – ManageNow® Data Analytics ........................................................................10

Decisively Australia – Legislation to Execution. Automating Decision-making Processes .........................10

Atos – Big Data Analytics for Risk Assessment in Border Management Context, Migration Analysis and 
Forecasting ........................................................................................................................................................11

Almaviva Group – Artificial Intelligence Available Solutions for Justice and Home Affairs Challenges .....11

Panel Discussion and Q&A ..............................................................................................................................................12

Session II - 11 November 2021. Privacy-preserving Approaches and Technologies for Data 
Analytics and AI

Keynote Presentation: Accountability for AI Use in the Area of Security .....................................................................14

Industry Presentations ....................................................................................................................................................15

Leonardo – Embedding Privacy in Data-driven Digital Transition Processes................................................15

GMV – uTile PET................................................................................................................................................15

secunet Security Networks – Privacy by Design .............................................................................................16

Panel Discussion and Q&A ..............................................................................................................................................17

Session III - 18 November 2021. Artificial Intelligence in IT Operations – AIOps

Industry Presentations ....................................................................................................................................................19

NTT Data – AIOps as Autonomous IT Operations Facilitator .........................................................................19

PWC EU Services – Shared AI Platforms. Experience from European Piloting .............................................19

Sopra Steria Benelux – AI for IT Ops ................................................................................................................20

Session IV - 25 November 2021. Artificial Intelligence in Biometric Recognition Technologies

Project Presentations ......................................................................................................................................................22

AI Supporting the Evaluation of Face Recognition Technology ....................................................................................22

Technology Foresight on Biometrics for the Future of Travel .......................................................................................23

Industry Presentations ....................................................................................................................................................25

IDEMIA – AI & Biometrics .................................................................................................................................25

Fujitsu – Palm Secure: Use Your Hand to Unlock the World...........................................................................25

Mobai – Industry Perspective on Face Morphing and Mobile ID Checks ......................................................26

secunet Security Networks – Morphing Attack Detection in Practice ...........................................................27

Panel Discussion and Q&A ..............................................................................................................................................28

Closing Remarks ..............................................................................................................................................................30

Industry Roundtable November 2021 Reporteulisaroundtable.euIntroduction

4

Throughout November 2021, eu-LISA, in collaboration 
with  the  Slovenian  Presidency  of  the  Council  of  the 
EU, hosted its 14th Industry Roundtable under the title 
“Artificial  Intelligence  and  Large-Scale  IT  Systems: 
Opportunities and Challenges”. The event, conducted 
entirely online, brought together over 310 participants 
from  39  countries,  representing  international  border 
management  and  security  companies,  EU  Member 
State authorities, EU Institutions and JHA Agencies, as 
well as representatives of NGOs and academia.

The  event  took  place  in  four  sessions  focusing  on 
different  applications  of  artificial  intelligence  and 
privacy-preserving technologies, including:

1)  Artificial Intelligence for Advanced Data 
Analytics and Forecasting to Support 
Business Processes in the Area of Internal 
Security

2)  Privacy-preserving Approaches and 

Technologies for Data Analytics and AI

3)  Artificial Intelligence in IT Operations – AIOps

4)  Artificial Intelligence in Biometric Recognition 

Technologies

This  summary  report  provides  an  overview  of  the 
academic  and  industry  presentations,  as  well  as 
stimulating  discussions  that  took  place  during  the 
event.

Industry Roundtable November 2021 Reporteulisaroundtable.euSession I: 4 November 2021 – 13:00 – 16:30 (CET)

AI for Advanced Data Analytics and Forecasting 
to Support Business Processes in the Area of 
Internal Security

Session I

Artificial Intelligence for Advanced Data 
Analytics and Forecasting to Support Business 
Processes in the Area of Internal Security

4 November 2021

Opening Remarks by Krum Garkov,
Executive Director of eu-LISA

6

in 

Mr  Krum  Garkov,  Executive 
Director 
eu-LISA, 
of 
welcomed  the  participants 
of  the  Industry  Roundtable 
emphasising  that  Artificial 
Intelligence  (AI)  is  one  of 
the key priorities for political 
decision-makers 
the 
EU.  In  order  to  understand 
the  importance  of  AI  as 
technology, 
a  disruptive 
it  needs  to  be  considered  in  the  broader  context  of 
digital transformation. In recent years, and in particular 
since the onset of the pandemic, all areas of our lives 
have  moved  online  and  become  increasingly  digital. 
Mr  Garkov  illustrated  this  statement  by  mentioning 
that, for example, 90% of data available on the internet 
today, was created in the past three years; every minute 
more than 300 hours of video content is uploaded on 
YouTube; current prototypes of driverless cars that are 
heavily reliant on AI, generate 1 Gb of data per second; 
this year alone, 1.4 billion smartphones will be shipped 
worldwide; and in the next five years, there will be more 
than 50 billion connected devices. These are still rather 
conservative  estimates,  considering  the  exponential 
development of digital technologies.

Mr Garkov continued his introductory remarks stating 
that the implementation of AI is not a technical issue 
– the technology exists and it often works. However, 
in order to ensure that AI is implemented in a robust 
manner, we need to have a solid and flexible legislative 
framework  in  place,  that  is  encouraging  rather  than 
restrictive  when  it  comes  to  the  implementation  of 
AI  solutions,  while  also  providing  balance  between 
the capabilities of AI and the respect of fundamental 
rights,  privacy,  data  protection,  equality  and  non-
discrimination,  etc.  Mr  Garkov  stressed  that  all 
technologies,  and 
implementation,  are  as 
successful as the level of trust in their capabilities in 
the society at large. Building on that, eu-LISA welcomes 
the proposed regulatory framework for AI presented by 
the European Commission in April 2021.

their 

Focusing  on  the  importance  of  AI  for  the  domain  of 
justice  and  home  affairs,  Mr  Garkov  mentioned  a 
few  important  use  cases  for  the  implementation  of 
AI,  which  include:  automation  of  routine  tasks  and 

repetitive processes that need to handle large amounts 
of  data;  automated  translation;  advanced  analytics; 
in-depth  risk  analysis,  etc.  Ultimately,  AI  has  a  great 
potential  to  increase  the  efficiency  of  border  guards, 
law  enforcement  officers,  migration  officers  and  the 
judiciary across Europe. 

Going forward, Mr Garkov emphasised that AI is not a 
threat in itself, as it will not replace border guards or 
police officers, but will help them to be more efficient 
and make better decisions. Therefore, when it comes 
to regulations, it is important to bear in mind that they 
focus  on  the  specific  application  of  technologies, 
not  technologies  themselves.  eu-LISA  is  and  will 
continue to be at the heart of these new and exciting 
developments, in particular by identifying AI application 
use cases and implementing relevant solutions in our 
domain  of  responsibilities.  To  that  end,  the  Agency 
is  committed  to  evolve  as  a  centre  of  excellence  for 
AI  in  the  JHA  domain,  as  recently  proposed  by  the 
Commission, and will continue building the necessary 
capabilities. Undoubtedly, the Agency will not be able 
to do this alone, but only in close cooperation with the 
Member States, European Institutions and the industry.

In  conclusion,  Mr  Garkov  stated  that now  is the time 
to  act,  with  a  view  to  developing  the  EU’s  strategic 
independence  in  this  domain.  In  order  to  do  that,  Mr 
Garkov suggested, we would need to tackle three issues 
that lie in the way to successful implementation of AI 
solutions. First, we need to ensure that the regulatory 
framework at the EU and MS level is in alignment with 
the capabilities of AI, and enables rather than prevents 
the adoption of AI solutions. Second, we need to invest 
substantial resources in training and capacity building 
of  practitioners  who  will  be  applying  AI  in  their  daily 
work,  because  the  benefits  can  only  be  reaped  when 
users  understand  how  AI  works  and  what  are  its 
limitations. Finally,  and  most  importantly,  we  need  to 
build trust within society, that AI capabilities will bring 
added  value,  improve  internal  security  and  access  to 
justice, make border management more efficient, while 
also ensuring that these capabilities are applied in an 
ethical  and  lawful  manner.  Failing  to  address  these 
challenges  will  limit  our  ability  to  reap  the  benefits 
inherent in AI.

Industry Roundtable November 2021 Reporteulisaroundtable.euAI for Law Enforcement:
The Slovenian Experience

Mr Aleksander Pur, Senior Police Superintendent, IT and Telecommunications Office, Slovenian Ministry of the Interior

7

Mr  Aleksander  Pur,  Senior 
Police Superintendent from 
the  Slovenian  Ministry  of 
Interior,  welcomed 
the 
participants  on  behalf  of 
the  Slovenian  Presidency 
of  the  Council  of  the  EU. 
In  his  opening  remarks,  Mr 
Pur  presented  an  overview 
of the Slovenian experience 
law 
in  using  AI 
enforcement purposes. He emphasised that Slovenian 
authorities have long recognised the importance of AI 
and have supported the Commission in this endeavour 
by organising a workshop on AI for law enforcement.

for 

learning  techniques 

Setting  the  scene,  Mr  Pur  suggested  that  due  to  the 
large  amount  of  information  that  is  being  produced 
every  minute, 
including  by  numerous  cameras 
and  sensors,  AI  is  becoming  indispensable  for  the 
effective  performance  of  duties  by  law  enforcement 
authorities.  Mr  Pur  continued  by  outlining 
the 
definition of AI systems according to the proposed AI 
Act,  which  includes  a  wide  range  of  software  based 
on  novel  machine 
including 
deep  learning,  as  well  as  older  approaches  such 
as  expert  systems.  This  means  that  many  systems 
used by law enforcement authorities for investigative 
purposes, can be classified as high-risk systems that 
must  meet  specific  requirements.  The  strength  of  AI 
systems lies in the fact that they can uncover hidden 
relationships by analysing large amounts of data from 
various  sources,  thereby  improving  the  efficiency 
of  investigations  by  identifying  relevant  information 
in  large  amounts  of  unstructured  data.  At  the  same 
time, the main weakness of AI is that their decisions 
are not always reliable nor explainable. Therefore, Mr 
Pur  argued,  important  decisions  must  be  based  on 
transparent  AI  systems  and  all  information  must  be 
confirmed by humans.

Mr  Pur  continued  his  presentation  stating  that  on 
the  one  hand,  AI  systems  provide  opportunities  for 
improving performance of law enforcement and public 
security. On the other hand, unethical and illegal use of 
AI systems brings new threats, such as deepfakes, or 
misuse of AI by terrorist groups and organised crime. 
The  easiest  way  to  address  these  threats  would  be 
to  ban  AI  systems.  However,  in  that  case  we  would 
also prohibit the use of AI applications that can help 
identify victims of sexual abuse.

Hence,  the  easiest  way  is  not  always  the  best  way. 
Therefore,  Mr  Pur  argued,  we  need  to  improve  our 
understanding of AI. To illustrate his point, he offered 
the  following  example  –  machine  learning  systems 
often  rely  on  historical  data  to  predict  the  future; 
however,  in  crime,  the  modus  operandi  can  change 
daily,  and  therefore  patterns  from  the  past  are  not 
necessarily  relevant  in  the  future.  Mr  Pur  stated 
unambiguously  that  we  need  to  steer  clear  from 
black-box  solutions  that  provide  outputs  that  cannot 
be  explained.  However,  this  doesn’t  mean  that  we 
should  abandon  AI  as  such,  as  there  are  numerous 
techniques  that  provide  transparent  and  explainable 
results. When used appropriately, such techniques are 
almost risk-free. Therefore, he argued, understanding 
different AI techniques is paramount for their effective 
implementation in an ethical and trustworthy manner.

To  expand  on  this  argument  further,  Mr  Pur  offered 
an  example  of  a  system-based  on  machine  learning 
techniques  for  named  entity  recognition,  which 
helps  investigators  identify  links  between  different 
natural persons and legal entities in a large corpus of 
unstructured data. According to the proposed AI Act, 
such  a  system  would  still  be  classified  as  high-risk, 
despite  the  fact  that  the  system  only  provides  input 
for  human  decision-making  process.  Furthermore, 
AI systems can in fact enhance privacy, for example, 
by  anonymising  documents  through  the  removal  of 
named entities.

Mr  Pur  also  presented  an  example  focused  on  the 
Advance Passenger Information (API) and Passenger 
Name  Records  (PNR)  data  and  the  identification  of 
suspicious passengers using historical data. Here, the 
AI system is used to detect patterns that are then used 
as additional information by the passenger information 
unit to identify whether certain passengers might pose 
a threat or not.

Concluding his presentation, Mr Pur emphasised that 
today we cannot imagine law enforcement authorities 
without  AI,  because  nowadays,  investigating  criminal 
activity involves huge amounts of data. The proposed 
regulatory framework for AI will result in a significant 
increase in paperwork needed to clear the use of high-
risk  AI  Systems.  Ultimately,  proper  understanding  of 
the  functioning  of  AI  systems  is  necessary  for  the 
ethical  and  trustworthy  application  of  AI  systems  in 
law enforcement.

Industry Roundtable November 2021 Reporteulisaroundtable.euProject Presentation:
ITFLOWS – IT Tools and Methods for 
Managing Migration Flows

Dr Cristina Blasi, Autonomous University of Barcelona

8

Dr  Cristina  Blasi  Casagran 
is  assistant  professor 
in 
EU  law  at  the  Autonomous 
University of Barcelona, and 
she holds a PhD in Law from 
the  European  University 
(Florence,  2015) 
Institute 
where 
specialised 
she 
in  EU  privacy  and  data 
law.  Dr  Blasi 
protection 
is  currently  coordinating 
a  H2020  project  IT  Tools  and  Methods  to  Manage 
Migration  Flows  (ITFLOWS)1  and  that  was  the  topic 
of  her  presentation  which  opened  with  a  short  video 
presentation2 outlining the key objectives of the project.

ITFLOWS is a H2020 project aiming to provide accurate 
predictions  and  practical  management  solutions  for 
migration  flows  in  the  EU  in  the  phases  of  reception, 
relocation,  settlement  and  integration.  The  project, 
which  was  launched  in  September  2020  and  will 
run  until  2023,  is  currently  in  the  early  development 
stage,  focusing  on  the  collection  of  data  that  will 
be  used  in  the  development  of  a  user-friendly  IT  tool 
–  the  EUMigraTool  –  created  to  support  NGOs  and 
municipalities in assisting migrants arriving to the EU. 
For policymakers, the project will translate the results 
of  the  data  analysis  and  the  developed  solutions  into 
practical  policy  recommendations  distributed  in  the 
form of policy briefs.

The  EUMigraTool  has  two  main  objectives:  first,  to 
offer  accurate  predictions  of  migration  flows;  second, 
to  identify  tensions  and  potential  violence  between 
migrants and local citizens in EU Member States.

a)  When  it  comes  to  predicting  migration  flows, 
the  project  focuses  on  the  countries  that  have 
historically  been  major  sources  of  migration  into 
the EU, intending to identify the drivers of migration 
from these countries. In addition, the project also 
analyses the countries that are targets of migration 
flows, namely Greece, Italy and Spain, delving into 

the specific reasons why these particular countries 
are most frequently chosen as main destinations. 
The data collected in the framework of this project 
will serve as input for the development of predictive 
algorithms. The preliminary objective is to predict 
migratory flows at least one week in advance, with 
the ultimate goal of making predictions for up to 1 
month in advance by the project’s end.

b)  The  component  developed  for  the  prediction  of 
tensions will focus on three Member States, namely 
Greece,  Italy  and  Spain,  and  will  use  sentiment 
analysis techniques in order to identify the general 
sentiment  around  the  topic  of  migration.  This 
will  be  done  using  a  wide  range  of  data  sources, 
including  data  on  public  attitudes  collected  via 
regular surveys, as well as real-time data, such as 
data  collected  from  Twitter,  Google  trends,  and 
GDELT3  which  is  particularly  helpful  in  identifying 
large-scale  events,  such  as  natural  disasters  and 
protests that may trigger migration flows.

In  order  to  ensure  that  the  EUMigraTool  meets  the 
requirements or user-friendliness and minimal technical 
knowledge, the project has engaged a number of NGOs 
and  municipalities  who  serve  on  the  project’s  board 
and will be actively involved in testing the tool once it’s 
ready. In addition, the project has also set up a policy 
working group that will be supporting the project from 
the policy perspective.

Dr  Blasi  continued  her  presentation  with  an  overview 
of  simulations of  public  sentiment  towards  migration, 
which can be used by municipal authorities and NGOs 
in  re-location  and  integration  efforts.  Additionally,  Dr 
Blasi also presented an analysis of data from refugee 
camps  that  can  be  used  by  national  authorities  in 
identifying  best  approaches  for  handling  migrants 
depending on their age group, gender, etc. Concluding 
her presentation, Dr Blasi mentioned that in June 2022 
the project will organise a workshop in Brussels for EU 
policymakers from Member States and institutions, as 
well as NGOs operating in the field of migration.

1 For more information, please visit the official website of the ITFLOWS project itflows.eu
2 The video is available online using the following hyperlink: youtube.com/watch?v=2KVKWuSQSZo
3 Global Database of Events, Language and Tone gdeltproject.org

Industry Roundtable November 2021 Reporteulisaroundtable.euQ&A Session

9

The  first  question  focused  on  whether  the  developed 
models will factor in the impact of extreme events such 
as military conflicts, and what role will AI play in such 
cases.

Dr  Blasi  responded  that  the  project 
is  currently 
collecting  data,  including  data  on  different  conflicts, 
that will be used to develop models to provide insights 
on the impact of extreme events (e.g., military conflicts) 
on migration flows. Dr Blasi went on to further explain 
that the tool would benefit from a manual entry system 
that would allow adding various events as they happen.

The second question inquired whether the EUMigraTool 
will  be  adaptable  to  other  countries  of  origin  and 
destination of irregular migration that are currently not 
included in the project.

Dr  Blasi  confirmed  that  the  EUMigraTool  will  be 
adaptable  to  other  countries  as  well.  Since  it  is 
impossible to address all EU countries within the scope 
of one project, the countries were chosen for practical 

reasons and based on discussions with experts. After 
the project is completed, the scope will be expanded to 
all 27 EU Member States, as the main aim of the project 
is  to  provide  a  practical  tool  for  NGOs  that  operate 
across the EU.

The final question addressed the issue of using Twitter 
as one of the main data sources, while excluding other 
social media platforms. Was this a data-driven choice 
based  on  the  use  of  specific  platforms  in  chosen 
Member States?

Dr Blasi argued that Twitter was chosen as a relevant 
data source, as it provides information on the intentions 
of  potential migrants, as  well  as  on  attitudes towards 
migration  in  the  countries  of  destination.  Ultimately 
the ambition is to include other social media platforms 
to  provide  a  more  comprehensive  picture  of  both  of 
migrant intentions and risks of tensions.

Industry Roundtable November 2021 Reporteulisaroundtable.euIndustry Presentations

10

Fujitsu Technology Solutions – ManageNow® Data Analytics

Mr Fritz Benker, Head of Platforms Operation Service Central & Eastern Europe

Mr Fritz Benker presented Fujitsu’s ManageNow data 
analytics platform that integrates a variety of structured 
and unstructured data sources in a data lake, enabling 
to  run  nearly  real  time  analytics  with  interactive 
dashboards.  The  solution  features  a  wide  variety  of 
data analytics tools and methods (both statistical and 
AI/ML),  as  well  as  predictive  capabilities  that  can  be 
used for predictive analytics in the operation of diverse 
systems. ManageNow can be deployed as a software-
as-a-service  (SaaS)  either  on  a  private  or  on  a  public 
cloud, and also onsite at the customer’s data centre.

Considering  that  ManageNow  is  a  data  analytics 
platform,  data  scientists  can  deploy  ML/AI  models 
developed 
in  different  environments,  such  as  R 
or  python,  and  use  the  interactive  dashboards  for 
presenting the outcomes of their predictive analytics. 

However,  it  is  important  to  note  that  ManageNow  is 
not an AI development environment.

Using a range of open source technologies that provide 
the  necessary 
functionalities,  Fujitsu  developed 
an  enterprise-level  solution  that  is  highly  scalable, 
allowing  the  addition  of  new  nodes  to  the  cluster 
without  downtime.  As  a  result,  ManageNow  solution 
can be run on a 24/7 basis, as updates can be installed 
without disrupting the workflows.

At the end of his presentation, Mr Benker introduced a 
few use cases, including one focused on IoT (Internet 
of Things) operations, which integrates dedicated IoT 
components relevant in industrial manufacturing, such 
as Bosch and Schneider Electric.

Decisively Australia – Legislation to Execution. Automating 
Decision-making Processes

Mr Joel Nation, Chief Technical Officer

Australian company Decisively focuses on helping the 
public  sector  make  better  decisions  using  artificial 
intelligence.  Mr  Nation  opened  his  presentation 
outlining  some  of  the  challenges  for  modern  public 
sector IT systems. First, most of these systems operate 
as  black  boxes,  resulting  in  scepticism  towards  the 
decisions  made.  Usually  such  systems,  even  if  they 
are  rules-based,  are  developed  by  IT  programmers, 
which  raises  questions  of  transparency.  As  a  result, 
there is often doubt whether the decisions made using 
automated systems are actually in line with legislative 
intent and current regulations.

Another  problem  is  that  the  standard  approach  is 
rather slow, often taking the double amount of time to 
develop a system as it takes to get legislation through 
parliament.  In  addition,  rules  tend  to  change  quite 
frequently,  meaning  that  the  systems  need  regular 
updates.  As  exemplified  by  the  COVID-19  pandemic, 
rules  can  change  very  quickly,  in  particular  when  it 
comes  to  borders  and  health.  Therefore,  we  need  to 
develop ways to implement such changes quickly, and 
analyse their impact on decision-making processes in 
IT systems. What is more, in this process it is important 
to  ensure  traceability,  i.e.  to  ensure  that  automated 
decisions can be traced back.

Decisively  has  essentially  decided  to  approach  such 
rules  as  a  service,  providing  a  centralised  service 
that  different  systems  can  use  to  make  accurate, 
transparent  and  auditable  decisions,  as  compared  to 

the current approach where different systems operate 
based  on  different  rules  that  are  often  hard  to  track 
and  reconcile.  Mr  Nation  explained  that  Decisively 
relies  on  natural  language  in  legislation  to  encode 
rules  that  will  be  used  in  decision-making  systems, 
allowing  decisions  to  be  linked  directly  back  to  a 
specific provision in a legislative act.

the  possibility 

Continuing  his  presentation,  Mr  Nation  outlined 
additional  functionalities  featured  in  the  Decisively 
to  perform 
system,  such  as 
counterfactual  or  ‘what  if’  analysis,  enabling  to 
evaluate policy changes in terms of their effectiveness 
and  efficiency.  The  rules-based  system  can  also  be 
complemented  with  an  AI  component,  for  example, 
to  perform  risk  assessment 
in  visa  application 
procedures,  where  an  AI  system  can  analyse  the 
behaviour  of  the  applicant  in  real  time  in  order  to 
identify  whether  the  applicant  is  trying  to  game  the 
system.  Other  functionalities  of  the  system  include 
integration  with  financial  analytics  tools  that  can 
perform simple automated analyses of bank account 
transaction  data;  document  analysis  using  OCR 
and  natural  language  processing  in  order  to  identify 
potential  fraud;  entity  matching  across  different 
documents, all of which are particularly relevant in the 
context  of  processing  visa  applications.  Concluding 
his presentation, Mr Nation stated that Decisively does 
not  use  AI  for  automated  decision-making,  instead, 
AI is used to support the decision-making process by 
providing relevant information and risk assessment.

Industry Roundtable November 2021 Reporteulisaroundtable.euAtos - Big Data Analytics for Risk Assessment in Border Management 
Context, Migration Analysis and Forecasting

Mr Michel Jardon, Public Sector Solution Manager

11

Mr Jardon opened his presentation with an overview of 
the Atos approach to integrated border management, 
which  is  divided  into  three  pillars:  control  points  at 
borders,  unregulated  borders,  trade  and  customs. 
The technologies developed by Atos were introduced 
by  way  of  specific  case  studies.  First,  the  asylum 
management  case  in  Germany,  where  Atos  provided 
the German Federal Office for Migration and Refugees 
with a system for asylum seeker validation to be used 
in cases when documents are incomplete or missing. 
The  system  integrates  voice  and  dialect  recognition, 
face recognition, as well as using the asylum seeker’s 
mobile phone to validate their origin in a multi-factor 
validation process. When it comes to language/dialect 
recognition,  the  system  can  distinguish  between  22 
languages and six dialects of Arabic.

Continuing his presentation, Mr Jardon introduced an 
AI  use  case  focusing  on  computer  vision  solutions 
for  regulated  and  non-regulated  border  crossing 
points that can be implemented in different scenarios 
with  CCTV  cameras,  such  as  airports,  public  spaces, 
green  borders,  etc.  The  system,  based  on  object 
identification  with  pre-defined  scenarios,  is  able  to 

detect  and  monitor  movement  of  people  and  goods 
(e.g.  unattended  luggage  in  airports  or  abandoned 
vehicles  in  public  spaces)  and  is  can  be  trained  to 
identify certain objects and the pre-defined scenarios 
in video stream.

incorporates  a  wide 

Going  forward,  Mr  Jardon  provided  an  overview  of  a 
data platform for implementing different data analytics 
solutions, including those mentioned before. The data 
platform  is  built  around  micro-services  architecture, 
range  of  supporting 
and 
technologies  such  as  federated  AI,  edge  computing 
and  blockchain,  where  those  are  relevant.  Mr  Jardon 
closed  his  presentation  with  a  discussion  of  ethical 
applications  of  AI  as  an  essential  component  when 
considering  the  implementation  of  AI.  In  particular, 
Mr  Jardon  emphasised  that  before  we  develop  and 
deploy an AI system, we should consider whether the 
technology will be used to benefit the public, whether 
the use of data is legitimate or are there possibilities 
for  misuse,  and  whether  data  analytics  provides  the 
necessary evidence that is unbiased and explainable.

-  Artificial 
Almaviva  Group 
Solutions for Justice and Home Affairs Challenges

Intelligence  Available 

Ms Giulia Zimei, International Business Development Manager
Mr Fabio Previtali, AI Solution Architect & Davide Buscaglia, Global Sales Account Manager

AlmavivA Group is one of the major and longstanding 
IT  service  providers  for  the 
Italian  Government 
and  Public  Administration,  in  the  areas  of  internal 
security, law enforcement and cyber security, pursuing 
innovation by integrating and evolving complex legacy 
systems  with  emerging  technologies.  Ms  Zimei 
outlined  the  overall  scope  of  activities  of  Almaviva 
Group  in  the  area  of  data  analytics  and  AI,  focusing 
on  two  central  aspects  of  the  JHA  domain:  a)  visa 
process  digitalisation,  and  b)  data  management  and 
semantic ontology data models.

For  the  visa  process  digitalisation,  Almaviva  has 
developed  an  AI  training  and  learning  automation 
platform  (T&LA),  enabling  the  automation  of  the  ML 
process  from  the  first  stage  of  acquiring  data  to  the 
deployment  of  a  model  in  production.  The  platform 
incorporates  the  full  range  of  machine 
learning 
algorithms, starting from the classical random forest 
to artificial neural networks. The platform also allows 
for  automatic  monitoring  of  models  in  production, 
removing the requirement for manual interventions.

This AI platform was further introduced through three 

use  cases.  First,  automatic  document  validation 
that  checks  the  document’s  validity,  incl.  names 
and  personal  identity  numbers.  Second,  document 
coherence  validation  that  performs  an  automated 
consistency  check  across  all  documents  submitted 
by  the  applicant,  including  whether  the  sequence 
of  document  submission 
is  correct  and  pattern 
identification to ensure that the documents are valid. 
Third,  automatic  verification  of  submitted  photos 
for  aging,  masking,  etc.,  using  convolutional  neural 
networks to identify changes.

In  the  final  part  of  the  presentation,  Mr  Buscaglia 
gave  an  overview  of  the  solutions  focusing  on  data 
management and semantic ontology data models that 
can be used to query data lakes containing structured 
and  unstructured  data  using  natural  language.  The 
proposed solution provides an overview of all possible 
connections  related  to  a  specific  entity  (e.g.  an 
individual  crossing  a  border  that  may  be  of  interest 
to  authorities).  Concluding  the  presentation,  Mr 
Buscaglia pointed out that the AI solutions provided by 
Almaviva can be integrated with voice biometrics and 
speech recognition.

Industry Roundtable November 2021 Reporteulisaroundtable.euPanel Discussion and Q&A

12

The 
panel 
concluding 
discussion  on  using  AI  for 
advanced data analytics in 
the area of internal security 
was led by Mr Igor Taranic, 
Senior  Research  Officer  at 
eu-LISA.

First, the issue of trust and 
social  acceptance  of  AI 
was addressed.

Mr  Jardon  (Atos)  drew  a  connection  to  larger  public 
mistrust  in  government  and  advocated  for  increasing 
trust 
in  government  to  help  citizens  realise  that 
innovative  technologies,  such  as  AI,  can  be  used  for 
the  common  good,  e.g.,  identifying  disturbances  in 
public  areas  using  video  streamed  data  from  CCTV. 
When  talking  about  deploying  the  Fujitsu  dashboard 
system in the border management context, Mr Brenker 
replied  that  this  area  of  application  is  very  similar  to 
law enforcement. The quality of dashboards ultimately 
depends on the quality of input data that is behind the 
dashboard.

The discussion continued with a question addressed to 
Decisively,  focusing  on  AI  capabilities  that  need  to  be 
developed to enable AI to interpret legislation.

Responding  to  this  question,  Mr  Nation  (Decisively) 
into 
indicated  that  two  aspects  must  be  taken 
consideration. First, there is not enough legislation for 
training AI systems. Second, legislation is very specific 
about requirements and conditions that need to be met 
in  order  make  a  certain  decision.  Therefore,  we  need 
to be able to test these AI/ML solutions and verify that 
they  make  the  right  decisions  according  to  the  rules 
contained in the legal acts.

Next,  the  panel  discussed  the  use  of  mobile/smart 
phones for identity validation.

Mr  Jardon  (Atos)  explained  that  mobile  phones  are 
used for identity verification of asylum applicants only 
in  cases  where  other  means  of  verification  are  not 
available.  Such  identity  verification  is  voluntary  and 
uses only data that has been posted publicly (e.g. social 
media  posts).  A  follow-up  question  addressed  the 
challenge of dialect verification to which to Mr Jardon 
(Atos) responded that the accuracy of dialect detection 
for Arabic is very strong, but this system is always used 
in combination with a human translator.

Finally,  the  panellists  discussed  the  areas  where  AI 
deployment is not yet possible.

that 

suggested 

(Decisively) 

Mr  Nation 
the 
implementation of AI application ultimately comes down 
to risk tolerance of decision-makers. In some areas, e.g., 
child protection or long-term visa applications, mistakes 
can  be  very  costly,  although  the  number  of  decisions 
steps  is  not  that  large  and  therefore  the  use  of  AI  is 
not  justified.  In  contrast,  AI  systems  can  be  deployed 
relatively safely in processing visa applications of regular 
travellers  or  tourists  where  associated  risks,  including 
wrong decisions, are relatively low. Mr Brenker (Fujitsu) 
seconded  the  point,  arguing  that  we  should  avoid  the 
application  of  AI  in  areas  where  its  efficiency  has  yet 
to be proven and where errors in automated decision-
making can have serious consequences for individuals. 
Mr  Nation  (Decisively)  followed-up  explaining  that  a 
significant  number  of  tasks  can  be  automated  in  any 
area, e.g., analysis of document authenticity, biometric 
matching, etc., and AI is useful in reducing the human 
workload  related  to  such  routine  and  repetitive  tasks. 
However, we should steer clear from using AI in areas 
and  tasks  where  human  decisions  are  indispensable, 
which on average constitute a relatively small share of 
all tasks.

Industry Roundtable November 2021 Reporteulisaroundtable.euSession I: 4 November 2021 – 13:00 – 16:30 (CET)

AI for Advanced Data Analytics and Forecasting 
to Support Business Processes in the Area of 
Internal Security

Session II

Privacy-preserving Approaches and 
Technologies for Data Analytics and AI

11 November 2021

Keynote Presentation: Accountability for AI 
Use in the Area of Security

Prof. Dr Saskia Bayerl, Centre of Excellence in Terrorism, Resilience, Intelligence and Organised Crime Research (CENTRIC)

14

of 

is  a 
Dr  Saskia  Bayerl 
Professor 
Digital 
Communication and Security 
at  the  Centre  of  Excellence 
in  Terrorism,  Resilience, 
Intelligence  and  Organised 
Crime  Research  (CENTRIC). 
Her  research  interests  lay  at 
the  intersection  of  human-
interaction, 
computer 
organisational  communication  and  change,  with  a 
special focus on ICT implementation, privacy, and the 
management of transparency.

Prof.  Bayerl  gave  a  presentation  of  her  research  into 
accountability  principles  for  AI  (AP4AI  Framework). 
The objective is to develop a robust set of agreed and 
validated framework of accountability principles for AI, 
integrating  a  wide  range  of  inputs  from  practitioners 
to regular citizens. Ultimately, the aim is to develop a 
practical compliance and assessment tool for security 
practitioners.  The  core  consortium  of  the  research 
project comprises CENTRIC, Europol, Eurojust, and the 
European Union Agency for Fundamental Rights.

The  AP4AI  Framework  project  is  divided  into  three 
(25 
distinct  cycles.  First,  expert  consultations 
countries)  and  review  of  state  of  the  art  (corpus  of 
135+  documents)  to  lay  the  groundwork.  Second, 
citizen  consultations  in  the  form  of  an  online  survey 
with approx. 6000 citizens from 30 countries (EU MS, 
UK, USA, Australia), and in the final stage, framework 
validation  by  subject-matter  experts.  Currently,  the 
first  cycle  is  near  completion  and  preparations  are 
underway for commencing citizen consultations.

The focal point of Prof. Bayerl’s research is the need 
for  an  AI  assurance  framework  in  order  to  develop 
public  trust  in  a  system  that  carries  considerable 
risks  and  harms.  To  be  effective,  accountability 
must  be  bound  to  enforceable  obligations,  making 
it  actionable.  In  a  nutshell,  accountability  provides 
a  practical  mechanism  to  ensure  that  legitimate 
interests  (as  well  as  concerns,  fears  and  hopes)  of 
all  stakeholders  are  factored  in  throughout  the  full 
decision-making  process  about  the  AI  capabilities 
used by law enforcement agencies.

While 
there  are  some  existing  AI  frameworks 
developed  by,  for  example,  the  European  Parliament, 
Council of Europe, OECD, Interpol and several national 
governments,  e.g.,  UK,  Australia,  Canada,  Germany, 
etc.,  perhaps  the  most  comprehensive  is  the  one 
prepared  by  the  European  Commission’s  High-Level 
Expert  Group  on  AI.  In  2019,  this  group  pinpointed 
accountability  among  seven  key  principles  for  AI, 
defining it as mechanisms to ensure responsibility and 
accountability for AI systems and their outcomes.

Finally, Prof. Bayerl introduced some select propositions 
stemming from her research, such as having a double 
structure  on  EU  level  and  national  level  (e.g.,  akin  to 
the  human  rights  framework),  broad  compliance  by 
organisations  with  appropriate  remedies  in  place  for 
individuals,  exceptions  for  emergencies,  although 
generic  ‘national  security’  justifications  would  not  be 
acceptable as they are too vague.

In  her  concluding  remarks  Prof.  Bayerl  stressed  that 
this  is  not  a  one-off  effort  and  going  forward,  she  is 
looking  into  the  creation  of  support  mechanisms  for 
AP4AI  assessments,  in  conjunction  with  continuing 
consultations  and  evaluations  for  updates  and 
improvements.

During  the  subsequent  Q&A  session,  Prof.  Bayerl 
addressed questions regarding accountability and AI.

The  first  question 
accountability and innovation in AI.

inquired  about  balancing 

To start, Prof. Bayerl stressed the need to distinguish 
between  using  AI 
in  specific  high-risk  domains 
and  a  universal  approach  to  AI.  On  the  other  hand, 
accountability  should  not  be  seen  as  counter  to 
innovation.  In  fact,  the  two  are  closely  intertwined 
because  accountability  entails  responsibility  to  keep 
improving  the  systems  for  increased  benefits.  So 
actually,  accountability  and  innovation  complement 
each other.

The second question addressed the issue of high-risk 
systems and the need for more rigorous accountability 
in such contexts.

According to Prof. Bayerl, in such cases it is paramount 
to ensure proper governance and conduct a thorough 
analysis  of  the  risks.  All  innovation  entails  risk,  but 
we must decide what risks we are willing to take and 
who will carry the risks – those are issues that must 
be addressed and injustices redressed. Another good 
question to ask is what are the risks or consequences 
if  we  don’t  go  forward  with  the  proposed  innovation. 
Elaborating  on  a  question  from  the  audience  with 
regard to who defines the risks, Prof. Bayerl stressed 
that  risks  must  be  defined  collectively  by  involving 
different groups.

When asked whether accountability might limit AI,

Prof.  Bayerl  argued  that  it  is  not  a  limiting  approach 
at  all,  and  the  aim  is  to  set  it  up  as  an  empowering 
approach  that  ensures  the  use  of  AI  in  a  way  that 
doesn’t  create  any  problems.  As  such,  accountability 
is  an  important  part  of  responsible  AI  use,  offering 
a  valuable  framework  to  guide  operation  and  further 
development.

Industry Roundtable November 2021 Reporteulisaroundtable.euIndustry Presentations

15

Leonardo – Embedding Privacy in Data-driven Digital 
Transition Processes

Ms Arianna Pedrini, Security Governance (EU Institutions and Agencies)

Leonardo is a global technology company operating in 
the areas of aerospace, defence and security, offering 
global security solutions for both military requirements 
and  civil  applications,  ensuring  the  security  and 
resilience  of  physical  and  digital  infrastructures  via 
complete protection against hybrid threats and critical 
events.

Modern  organisations  faced  with  an 
increasing 
amount of different norms and they need a one-stop- 
shop  solution  for  managing  an  increasing  variety  of 
risks.  Ms  Pedrini  presented  several  compliance  risk 
assessment tools that ensure accountability by design 
and  come  with  embedded  data  protection  features, 
pre-defined checklists, alerts, etc. These tools are well-
suited  to  monitor  processes  over  longer  periods  and 
generate  recommendations  for  devising  prioritised 
action plans.

For operations, Leonardo has developed an AI decision 
support  system  (DSS),  a  cybersecurity  platform  that 
provides  real-time  dynamic  overview  and  situational 
awareness, making use of AI to pull together different 
sources of data to support decision making. Given an 
infrastructure  to  monitor,  AI  DSS  collects,  correlates 
and analyses data to build a comprehensive overview 
of  cyber  risks,  providing  organisations  with  decision-
making support that helps ensure business continuity 
and minimising cyber impacts.

Another data protection tool developed by Leonardo is 
the Privacy Query Platform (PQP) – a data protection 
layer  based  on  Machine  Learning/Natural  Language 
Processing  that  can  be  used  to  safeguard  citizen 
privacy  for  datasets  that  are  accessed  by  external 
entities for statistical or research purposes.

GMV – uTile PET

Mr Luis Grañana Martín, Business Partner & uTile Product Owner

Mr Grañana’s presentation focused on data protection 
solutions  in  the  context  of  data-sharing.  GMV  has 
developed  a  tool  called  uTile  that  facilitates  secure 
data-sharing between organizations, allowing machine 
learning (ML) algorithms and analytical models to be 
developed  by  using  sensitive  data  from  distributed 
it  enables  making  secure 
sources. 
calculations  based  on  distributed  sensitive  data 
without exposing or moving it.

In  addition, 

With uTile, ML models are trained locally using federated 
learning. Federated learning is an algorithmic solution 
that trains ML models by sending copies of a model to 
the place where data resides and performing training 
at the edge, thereby eliminating the necessity to move 
large amounts of data to a central server for training 
purposes, i.e., raw data never leave host premises and 
all connections are run through coordination nodes.

To  provide  stronger  security  guarantees  to  data 
owners,  uTile  uses  Secure  Multi-Party  Computation 
(SMPC) and Private Set Intersection (PSI), a powerful 
cryptographic  technique  which  enables  interested 

parties  to  find  intersections  between  their  datasets 
without  exposing  their  raw  data  to  the  other  party 
(such as location, ID, card number, etc).

During his presentation, Mr Grañana presented several 
use  cases.  For  instance,  an  example  of  federated 
learning  where  a  central  server  orchestrates  the 
training  of  a  model  based  on  input  from  three  data 
owners. Another example was of encrypted inference 
where  the  data  owner  requests  an  inference  result 
from  the  model  over  the  Internet  but  retains  the 
confidentiality  of  patient  data.  Other  applications 
include,  for  example,  data  owned  by  independent 
entities  or  different  units  within  the  same  entity  in 
various  jurisdictions  or  confidential  data  about  the 
same type of entities/objects distributed across silos, 
etc.

Industry Roundtable November 2021 Reporteulisaroundtable.eusecunet Security Networks – Privacy by Design

Mr Dominik Lawatsch, AI Business Development Manager
Ms Sina Youn, Senior Privacy & Technology Strategist @ brighter.AI

16

secunet is Germany’s leading cyber security company, 
offering  a  combination  of  products  and  consulting 
services, including extensive expertise in sophisticated 
and sustainable border control solutions for capturing 
and processing biometric data, as well as solutions for 
mobile identity checks and verification.

Mr  Lawatsch  presented  an  overview  of  AI  solutions 
developed by secunet for anonymised image and video 
analysis using privacy-focused artificial intelligence to 
demonstrate  how  to  reconcile  the  use  of  photo  and 
video data with data protection requirements.

To  train  AI  models,  we  need  large  amounts  of  data 
that  is  as  free  as  possible  from  artefacts.  However, 
the  storage  and  processing  of  such  personal  data 
is  subject  to  restrictions,  although  GDPR  (recital  26) 
allows for an exception for anonymous information.

The  solution  developed  by  secunet  –  Deep  Natural 
Anonymization  –  uses  AI-based  anonymization 
that  detects  and  replaces  captured  face  imagery 
automatically  with  artificially  created  natural-looking 
“masks”, thereby removing personal data and resulting 
in  natural,  high-quality  anonymization  of  persons.  As 
a  result,  the  evaluability  of  metadata  remains  intact 
while  the  protection  of  personal  identity  is  ensured. 
For example, compared to precision blurring which is 
re-constructible,  Deep  Natural  Anonymization  leaves 
sociographic and situational features intact, and is not 
re-constructible.

Industry Roundtable November 2021 Reporteulisaroundtable.euPanel Discussion and Q&A

17

technologies 

The 
panel 
concluding 
discussion focusing on privacy-
preserving 
for 
data  analytics  was  led  by  Dr 
Aleksandrs  Cepilovs,  Capability 
Building  Officer  -  Research  and 
Development at eu-LISA.

discussed 
whether it is possible to ensure complete privacy and 
data protection using current technologies.

panellists 

The 

In  addition, 

the  panellists  found 

cases. For example, GMV using AI federated learning 
in  combination  with  Secure  Multi-Party  Computation 
(SMPC). 
that 
federated learning has great potential, especially since 
it  creates  opportunities  for  international  cooperation 
by  facilitating  data-sharing  from  distributed  sources. 
However, in such instances it is important to balance 
privacy and security considerations with performance 
needs,  with  one  panellist  suggesting  synthetic  data 
built by AI as a possible solution.

The  panellists  agreed  that  protection  is  never  100% 
guaranteed  and  the  risks  are  always  there.  In  this 
situation,  it  is  important  to  find  the  right  balance 
between risks and benefits. Standards and regulations 
(e.g.  anonymization)  can  help 
in  this  regard  by 
providing  a  guiding  framework  and  AI  can  help  in 
achieving  these  objectives  (e.g.  federated  learning) 
by  delivering  a  very  high  level  of  data  protection.  As 
such,  public  sector  can  lead  the  way  and  promote 
these kinds of technologies for the benefit of society. 
Considering  the  EU’s  achievement  in  its  approach  to 
data protection, the next step could be certification to 
ensure public trust in AI systems.

Subsequently,  the  panellists  were  asked  to  elaborate 
on the topic of federated learning and its applications. 

The  panellists  were  in  agreement  that  the  most 
suitable  approach  is  determined  by  individual  use 

Lastly,  a  question  from  the  audience  regarding 
anonymized  images  and  videos,  specifically  how  to 
prevent  accidental  similarities  with  real  persons  (or 
license plates).

Responding  to  this  question,  Mr  Lawatsch  and  Ms 
Youn (secunet) explained that secunet’s Deep Natural 
Anonymization technology automatically anonymizes 
faces  and  licence  plates  in  images  and  videos  while 
information  and  context 
retaining  relevant  visual 
intact. In theory this means that contextual information 
could be used to identify anonymized objects but the 
probability  of  that  exact  licence  plate  in  combination 
with that exact car type occurring in real life or being 
traceable is extremely low. In case of anonymization, 
the threshold is at the difficulty of reconstructing the 
original identity, which must require high effort.

Industry Roundtable November 2021 Reporteulisaroundtable.euSession I: 4 November 2021 – 13:00 – 16:30 (CET)

AI for Advanced Data Analytics and Forecasting 
to Support Business Processes in the Area of 
Internal Security

Session III

Artificial Intelligence in IT Operations – AIOps

18 November 2021

Industry Presentations

Moderated by Ms Anna Beata Kolodziej, IT Officer, Business Relations Management 
Sector, eu-LISA

19

NTT Data – AIOps as Autonomous IT Operations Facilitator

Mr Luis Cancela Vallespín, Manager, Infrastructure & Cloud Services

Mr Vallespín’s presentation focused on how AIOps can 
drive  operational  excellence  trough  the  optimisation 
of  technology  and  operations.  In  this  context,  an 
important  concept 
is  IT  operations  maturity.  By 
evaluating  the  maturity  level  of  IT  operations,  it  is 
possible to chart a course to achieve the highest level of 
maturity – autonomous service. The first step towards 
autonomous  IT  operations  is  digitalisation  through 
transforming IT processes by eliminating routine work. 
This stage entails IT service orchestration (incl. service 
catalogue  automation)  and  defining  IT  operations 
as  architecture  (incl.  standard  frameworks)  to  yield 
such benefits as cost reduction, agility, scalability and 
overall  quality  improvement.  The  next  step  is  AIOps 
as  the  key  facilitator  for  autonomous  IT  operations 
by combining big data and AI functionality to provide 
data-driven  decisions  and  trigger  digital  operations 
routines.  As  a  result,  it  automates  broad  range  of 
operational  processes  and  tasks,  incl.  performance 
monitoring, event analysis, service management, and 
automation.

Thus,  autonomous  operations  are  achieved  by 
combining  digital  operations  with  AIOps  to  achieve 
operational  excellence.  This 
three 
stages.  First,  data  is  aggregated,  then  analysed,  and 
finally,  the  results  are  acted  upon,  i.e.,  automated 
pattern  discovery,  anomaly  detection,  root  cause 
determination, prescriptive advice, etc.

is  done 

in 

Mr  Vallespín’s  went  on  to  outline  six  main  benefits 
of  AIOps  platforms:  1)  better  causality  (to  identify 

probable causes of incidents), 2) reduced noise (e.g., 
false  alarms),  3)  data-driven  decision-making,  4) 
proactive  problem  resolution,  5)  improved  anomaly 
detection 6) extrapolation of future events to prevent 
potential breakdown.

Next, Mr Vallespín presented several AIOps use cases 
for IT service management (incl. auto triage, etc.), for 
operations  and  support  (incl.  end-to-end  monitoring, 
automated  root  cause  analysis,  anomaly  detection, 
capacity  management,  etc.),  and  for  service  desk 
support,  AIOps  provides  digital  assistant  (chatbots) 
that  helps  save  30%  of  time  dedicated  to  end-user 
requests.  Overall,  AIOps  contributes  to 
incident 
avoidance and improves resolution time (40%).

To conclude his presentation, Mr Vallespín presented 
an operational intelligence solution developed by NTT 
Data  –  Flens,  a  real-time  modular  and  multilayer  Big 
Data solution that enables the application of AI to each 
business layer for making predictions and identifying 
anomalies.  Its  automated  decision-making  enables 
achieving  maximum  operational  efficiency  and 
productivity, while also reducing operational noise and 
complexity.

At the end of the presentation Mr Vallespín was asked 
to give a real example how AI has helped pro-actively, 
and  he  responded  that  the  main  advantage  is  in  the 
anticipation  of  anomalies.  Also  improved  capacity 
management as a result of constant analysis of data, 
which enables better planning of resources.

PWC EU Services – Shared AI Platforms. Experience from 
European Piloting

Mr Andreas Braun, Director, PwC Luxembourg

Mr  Braun  provided  an  overview  of  the  collaborative 
Advanced  Data  Analytics  Platform  developed  for 
the  European  Commission,  a  process  covering  the 
whole  chain  from  prototyping  to  active  use.  The 
objective was to develop a central AI solution for risk 
assessment that feeds from a multitude of distributed 
data  sources  and  must  interface  with  systems  at 
different IT maturity levels.

The  result  was  an  onsite  collaborative  AI  platform 

based  on  an  “Infrastructure  as  Code”  composed 
of  +40  servers,  offering  seamless  integration  of  15 
open source and proprietary solutions, built around a 
tailor-made  portal  and  the  common  goal  to  work  on 
advanced AI matters.

The  platform  includes  the  following  key  features:  1) 
strong data governance and platform security (secure 
and  hermetic  projects  environment  with  restricted 
access); 2) vendor agnostic, allowing all components 

Industry Roundtable November 2021 Reporteulisaroundtable.euto be replaced; 3) near-real time processing supports 
time-critical  decision-making;  4)  multi-data  source 
integration,  incl.  external  systems  managed  by  other 
parties; 5) collaboration between different stakeholders 
with secured access control; 6) production-ready.

The developed AI platform enables the Commission to 
support MS in the design and implementation of real 
use  cases,  where  added  value  can  be  derived  from 
AI  advanced  analytics  and  near  real-time  analyses, 
e.g.  tailored  reports  and  dashboards  for  crisis 
management, machine learning capabilities, etc.

Having  concluded  his  presentation,  Mr  Braun  was 
asked  to  elaborate  on  the  mechanism  to  protect 
restricted data. Responding to this question, Mr Braun 
pinpointed  data  virtualisation and  data  encryption  as 
key elements of data protection. By design, all created 
datasets  are  made  available  only  to  the  designated 
user  group.  There’s  no  such  thing  as  public  data  by 
default, and access is granted only on a case-by-case 
basis. All data is restricted, and opened up only upon 
necessity to select users.

20

Sopra Steria Benelux – AI for IT Ops

Mr Simon Marsol, Director Public Sector
Mr Thierry Luc, CTO - Infrastructure and Security Services

leader 

is  a  European 

Sopra  Steria 
in  digital 
transformation  with  a  comprehensive  portfolio  of 
end-to-end  service  offerings:  consulting,  systems 
infrastructure 
integration,  software  development, 
management  and  business  process  services.  In  the 
internal  security  domain,  Sopra  Steria  contributes  to 
further developing centralised databases and services 
around  border  control  and  migration,  both  for  EU 
institutions and MS.

AIOps  combines  big  data  and  machine  learning  to 
automate  IT  operations  processes  and  the  main 
use  cases  for  AI  operations  include  performance 
analysis, 
incident/anomaly  detection,  causality, 
and  automation,  while  benefits  include  enhanced 
performance  monitoring,  predictability  of  incidents, 
capacity planning, incident resolution, etc.

Sopra  Steria  provided  an  overview  of  three  AI  Ops 
use  cases.  First,  AIOps  for  end-user  support  to 
improve  performance  of  support  agents  and  offer 
next  generation  support  to  end-users  (incl.  chatbot) 
by  providing  a  seamless  user  journey.  As  such,  it  is 
basically  a  decision-making  tool  allowing  real-time 
monitoring of service quality, using AI technology and 
virtual assistants.

Second, AIOps for infrastructure operations to facilitate 
transition from managing traditional IT to new IT that is 
performance oriented and based on user experience, 
characterised  by  standardisation,  automation,  data 
analysis and management (AI), predictive models, etc. 
As such, the end-to-end operations AIOps model is a 
platform  combining  several  tools  to  offer  a  range  of 
capabilities.  The  artificial  intelligence  engine  allows 
moving from silo supervision to end-to-end supervision 
across the value chain.

The final use case focused on AIOps for cybersecurity 
operations,  covering  such  features  as  automation, 
machine  learning,  incident  detection  and  response, 
vulnerability management and threat intelligence.

Overall,  AIOps  facilitates  increased  efficiencies  and 
higher  quality  services  through  business  monitoring 
based on end-to-end supervision, while also leading to 
a true transformation of teams.

Industry Roundtable November 2021 Reporteulisaroundtable.euSession I: 4 November 2021 – 13:00 – 16:30 (CET)

AI for Advanced Data Analytics and Forecasting 
to Support Business Processes in the Area of 
Internal Security

Session IV

Artificial Intelligence in Biometric Recognition
Technologies

25 November 2021

Project Presentations:
AI Supporting the Evaluation of 
Face Recognition Technology

Mr Haoyu Zhang, Prof. Christoph Busch – Norwegian Biometrics Laboratory (NTNU)

22

The research project run by Prof. Christoph Busch and 
Mr  Haoyu  Zhang  –  AI  Supporting  the  Evaluation  of 
Face  Recognition Technology  –  focuses  on  the  ability 
of  convolutional  neural  networks  to  generate  features 
in  facial  images  that  are  similar  to  bona  fide  ones.  In 
addition  to  eu-LISA  who  sponsors  the  project,  the 
consortium includes also Steinbeis Biometric Research 
Centre at Darmstadt University, Mobai, PLUS University 
in Salzburg, Austria, and NTNU.

The development and testing of biometric recognition 
systems  relies  on 
large-scale  biometric  datasets, 
and  although  there  are  many  public  ones  (e.g.,  India’s 
biometric  ID  system  Aadhaar  with  over  1bn  enrolled), 
their  use  is  limited  because  biometrics  are  deemed 
sensitive  personal  data  that  cannot  be  shared  with 
research  or  testing  institutions.  The  collection  of 
equivalent  datasets  is  technically  challenging  and 
very  resource-intensive,  and  its  use  is  similarly  bound 
by  privacy  restrictions.  However,  recent  advances  in 
convolutional  neural  networks  offer  a  way  forward  by 
enabling the generation of synthetic images that can be 
used for training biometric recognition systems.

The past five years have seen significant progress in the 
generation of synthetic facial images, that for a human 
being synthetic images have become indistinguishable 
from  real  ones.  This  research  aims  to  perform  an 
objective  evaluation  of  synthetic  images.  Standard 
testing methodologies have already been developed for 
fingerprint images (e.g., NFIQ2.1 and the corresponding 
standard  ISO/IEC  29794).  However,  when  it  comes  to 
facial 
industry-developed  proprietary 
algorithms  exist.  Thus,  in  order  to  have  a  vendor-
agnostic  approach,  it  is  necessary  to  develop  a  NFIQ 
equivalent  for  face  images.  Currently,  a  standard 
defining testing methodology for facial images is being 
developed  (ISO/IEC  29794-  5)  to  complement  the 
standards  on  biometric  sample  quality  (19794-5:2011 
and 39794-5:2019).

imagery,  only 

Mr  Haoyu  Zhang  presented  an  overview  of  the 
methodology  used  in  the  study  for  the  generation  of 
synthetic images and their evaluation. First, a database 
was  created  by  generating  non-mated  samples  using 
StyleGAN and StyleGAN2. In addition, a representative 
bona fide dataset was used from the Face Recognition 
Grand  Challenge  (FRGC).  Altogether  50,000  images 
were generated for each synthetic dataset with different 
truncation factors. These datasets were then evaluated 
using three different methodologies, including FaceQnet 
v1  (an  end-to-end  deep  learning  model),  SER-FIQ  (an 
unsupervised approach), and an implementation based 
on the ISO/IEC TR 29794-5:2010.

In the first stage, the evaluation of non-mated samples 
using the three approaches mentioned above produced 
the following results. The distribution of scores between 
FRGC and StyleGAN2 for FaceQnet are very similar, with 
the  FRGC  data  set  performing  slightly  better.  For  the 
ISO/IEC TR 29794-5:2010 implementation, distribution 
of  scores  are  also  very  similar,  with  the  StyleGAN2 
performing slightly better and more consistently. At the 
same time, the SER-FIQ approach shows that the FRGC 
data set performs better than the synthetic data set.

The second step was the evaluation of mated samples. 
In this case, when evaluating with FaceQnet, the FRGC 
bona  fide  dataset  exhibited  slightly  higher  quality. 
Similar results were attained when evaluating with SER-
FIQ, where FRGC data set evaluation resulted in higher 
score  distribution  than  synthetic  datasets.  However, 
when  evaluating  with  the  ISO/IEC  TR  29794-5:2010 
implementation,  synthetic  datasets  appear  to  have 
significantly higher quality. One of the takeaways from 
evaluating mated samples is that real datasets contain 
more  intra-identity  variability,  whereas  in  synthetic 
datasets,  only 
is 
introduced through semantic editing. Nevertheless, it is 
still relatively difficult to simulate intra-identity variance.

intra-identity  variability 

limited 

For  non-mated  samples,  the  applicability  of  synthetic 
data generated by StyleGAN and StyleGAN2 is similar. 
Only  minor  differences  exist  between  the  synthetic 
and  selected  set  of  bona  fide  samples,  meaning  that 
synthetic  facial  images  are  of  high  quality,  that  there 
are  minor  differences  in  estimated  biometric  sample 
quality,  and  that  the  variety  of  identity  information  is 
limited when the synthetic dataset is generated with a 
low truncation factor. As for mated samples, it is evident 
that  they  can  be  generated  without  any  significant 
loss  of  identity  information.  However,  one  challenge 
still  remains  –  bona  fide  data  has  higher  intra-identity 
variation than synthetic data.

Although  the  study  yielded  some  promising  results,  it 
is  still  not  sufficient  for  testing  operational  systems. 
There  are  two  essential  requirements  that  need  to  be 
met when testing operational systems before entry into 
operation. First, the system must work with acceptable 
transaction  times  or  throughput  rate,  which  is  usually 
tested with synthetic data as in this study. The second 
factor is biometric performance (FMR/FNRM) or, in case 
of identification mode, false positive and false negative 
identification rates. In this case we cannot extrapolate 
from  the  small  dataset  to  a  large  dataset  that  would 
exist in the operational system. Owing to that, there is 
no  viable  alternative  to  conducting  tests  using  actual 
large-scale  biometric  datasets,  however,  researchers, 
don’t have access to such large-scale data sets.

Industry Roundtable November 2021 Reporteulisaroundtable.euTechnology Foresight on Biometrics 
for the Future of Travel

Dr Luigi Raffaele, Research Officer, Frontex

23

Dr Luigi Raffaele is a Research 
Officer  at  Frontex’s  Research 
and  Innovation  Unit,  where 
he  oversees  the  development  and  implementation  of 
Research  and  Innovation  (R&I)  in  the  area  of  border 
security.

The project – Technology Foresight on Biometrics for 
the Future of Travel – was carried out during 2021 in 
collaboration with 9 EU MS and Schengen Associated 
Countries,  the  European  Commission,  eu-  LISA, 
Europol, the European Union Agency for Fundamental 
Rights, as well as Interpol, International Civil Aviation 
Organization  (ICAO),  and  the  U.S.  Department  of 
Homeland Security.

This  research  was  motivated  by  two  factors.  First, 
the  growing  cross-border  mobility  of 
individuals 
and  the  demand  for  seamless  border  crossing 
necessitates  digital  identity  management  solutions, 
as  well  as  no-gate  physical  solutions  for  automatic 
and  seamless  checks  at  border-crossing  points. 
Second,  the  COVID-19  pandemic  has  created  a  need 
for  technological  solutions  that  are  compatible  with 
policies  and  measures 
implemented  during  the 
pandemic, while also ensuring effective safeguarding 
of the EU’s external borders.

Owing  to  that,  biometric  technologies  are  the  key 
enabler  for  automated  processing  of 
individuals 
at  border  crossing  points.  This  foresight  project 
constitutes  an  anticipatory  response  with  a  view 
to  providing  the  European  Border  and  Coast  Guard 
community  with  knowledge  on  how  to  best  utilise 
biometric technologies in the future, as well as helping 
to  identify  research  and  innovation  activities  in  the 
area of biometrics in the short, medium and long term.

The analysis identified four ‘must-haves’ for biometric 
technologies:  1)  seamlessness;  2)  compliance 
with  fundamental  EU  values  and  regulations;  3) 
restrictions; 
applicability  within  pandemic-related 
4) 
low  vulnerability  to  adversary  attacks.  These 
characteristics  were  used  throughout  this  foresight 
project  as  reference  points  when  analysing  different 
biometric technologies.

The  review  of  the  state  of  the  art  in  biometric 
technologies  resulted 
in  a  three-level  taxonomy 
encompassing  57  biometric  technologies  grouped 
into  biomolecular,  morphological  and  behavioural. 
In  order  to  explore  these  technologies  at  system 
level,  the  taxonomy  was  augmented  with  a  two-
level  taxonomy  of  biometrics-enabled  technological 
systems. In addition to the state-of-the-art review, the 
project also performed a clustering of technologies on 
the  basis  of  patentometric  and  bibliometric  analysis, 
to  identify  most  active  parties  in  the  field  and  their 

geographical  distribution.  Not  surprisingly,  the  four 
largest technology companies (i.e. Microsoft, Amazon, 
Google and Apple) hold the largest number of patents 
in the area of biometrics.

Subsequently,  four  scenarios  for  the  future  of  travel, 
border  checks  and  biometrics  were  defined  with  a 
view to 2040 to be used in later stages of the project. 
Scenario  definition  was  followed  by  a  Delphi  survey, 
which  helped  prioritise  certain  technologies.  The 
experts  who  took  part  in  the  Delphi  survey  identified 
the  following  technologies  as  closest  to  mainstream 
and  having  a  relative  advantage  in  their  specific 
face 
face 
application: 
recognition; contactless friction ridge recognition; iris 
recognition in the NIR spectrum; iris recognition in the 
visible spectrum.

recognition;  3D 

infrared 

Following  the  Delphi  survey  and  prioritisation,  an 
in-depth  analysis  of  the  key  technological  clusters 
was  performed  with  the  aim  of  envisaging  future 
developments  in  terms  of  applications,  functions, 
products  and  systems.  This  phase  resulted  in  three 
main  outcomes:  visual  technology  roadmap;  list  of 
expected  key  opportunities  and  challenges  from 
today to 2040; and a comparative analysis of the key 
envisaged  developments  under  the  conditions  of  the 
four  scenarios.  This  was  further  complemented  by 
capability readiness analysis for each of the technology 
clusters, domains (i.e. research, industry, institutions), 
scenario, and in two time frames (2022-27; 2028-33). 

Q&A Session

The  first  question  inquired  about  the  most  reliable 
biometric modalities from the perspective of Frontex, 
and  what  kinds  of  combinations  with  biometrics  are 
being considered in addition to the EES requirements 
(i.e. facial imagery and fingerprints).

In response to this question, Dr Raffaele explained that 
the five technology clusters prioritised in the study are 
the  ones  that  Frontex  considers  as  the  most  reliable 
biometric modalities for application in border checks. 
These  modalities  are  also  in  alignment  with  the 
current mainstream modalities in industrial research. 
In  addition,  application  of  other  technologies,  such 
as  AI  and  privacy-preserving  or  privacy-enhancing 
biometric 
technologies, 
technologies,  will  further  improve  the  operational 
performance of border control systems.

combination  with 

in 

Responding  to  the  second  part  of  the  question, 
Dr  Raffaele  suggested  that  the  study  considered 
multi-  sensor  deployments  enabling  multi-modal 

Industry Roundtable November 2021 Reporteulisaroundtable.eubiometric recognition. The experts involved found that 
a combination of iris, fingerprint and facial recognition 
improving 
might  have  the  highest  potential  for 
biometric  accuracy,  with  3D  face  recognition  also 
relevant. In addition, iris in combination with periocular 
recognition was considered as relevant in combination 
with facial recognition. Another perspective from which 
multi-modal  combinations  were  considered  was  the 
contactless or remote biometric identification.

The  final  question  focused  on  whether  Frontex  is 
planning  to  pilot  some  of  these  modalities  in  real  life 
environments in the near future.

24

Dr  Raffaele  responded  that  at  this  point  in  time  it  is 
too early to speak about pilot projects. Before deciding 
on  possible  future  pilot  projects,  the  results  of  the 
technology foresight should be analysed first. At the EU 
level, under the Horizon Framework Programme, there 
will be calls to further advance research in the specific 
biometric  applications  for  seamless  border  checks, 
which Frontex strongly supports.

Industry Roundtable November 2021 Reporteulisaroundtable.euIndustry Presentations

25

IDEMIA – AI & Biometrics

Mr Vincent Bouatou, Head of Strategic Innovation, Public Security and Identity

IDEMIA  views  security  globally  by  factoring  in  the 
customer’s  environment  and  how  they  specifically 
use  technology.  In  a  world  of  ever-increasing  online 
interaction,  security  primarily  means  protecting 
identities, which is why IDEMIA focuses on Augmented 
Identity.

Mr Bouatou opened his presentation with an overview 
of how developments in AI have affected biometrics. 
First, the performance of face recognition algorithms 
has  improved  significantly  with  the  introduction  of 
AI,  in  particular  the  deep  learning  ML  algorithms, 
with  false  negative  identification  rate  moving  from 
3.89%  to  0.16%  between  2018  and  2021.  In  addition, 
AI  has  significantly  improved  robustness  against 
environmental  factors,  and  also  ageing.  It  has  also 
ushered  in  new  changes,  such  as  facial  recognition 
of  masked  individuals,  leading  to  threefold  reduction 
in  error  rates  within  six  months.  Another  area  where 
AI has had a significant impact is presentation attack 
detection.

improved 

Additionally,  AI  has  significantly 
the 
performance  of  consumer  grade  terminals  such  as 
smartphones, which can now be used for professional 
applications, such as biometric identification, which is 
a change in paradigm. This is also due to most modern 
high-end smartphones having a neuro-processing unit 
that  facilitates  fast  and  efficient  deployment  of  deep 
learning technology.

Furthermore,  AI  has  helped  improve  the  detection 
of  voluntary  or  involuntary  alteration  of  fingerprints. 
Thanks  to  AI,  biometric  recognition  is  now  used  for 
passenger facilitation, removing the need to present a 
physical identity document, allowing instead to rely only 
on biometric identifiers (incl. from one-to-one to one-
to-many  identification  without  loss  in  performance). 

Finally,  we  have  video  analytics  to  identify  persons, 
vehicle license plates, objects, etc.

Mr Bouatou continued his presentation suggesting that 
beyond  improvements  in  biometric  performance,  AI 
has also allowed us to enhance privacy protection. For 
example, anonymization of real-time video streams by 
removing people’s faces, or replacing individuals with 
avatars in a CCTV stream, thus removing all identifying 
information and maintaining the privacy of individuals 
captured in the video stream.

Mr  Bouatou  also  argued  that  AI  applications  in 
biometrics  have  taught  us  several  valuable  lessons. 
For  example,  from  the  perspective  of  the  GDPR, 
biometrics  have  always  been  considered  sensitive 
data, which has led to the development of technologies 
for processing biometric data (e.g., full homomorphic 
encryption,  secure  multiparty  computation,  etc.). 
The  area  of  performance  assessment  has  benefitted 
through having a range of standards on performance 
of  biometric  systems  already  available,  including 
standards  on  performance  testing  and  reporting, 
presentation attack detection, etc.

Concluding  his  presentation,  Mr  Bouatou  highlighted 
three key points. First, biometric recognition has been 
an  AI-based  technology  even  before  the  introduction 
of  deep  learning  algorithms.  Second,  AI  will  be  a 
critical  feature  of  applications  used  by  European 
authorities  for  ensuring  internal  and  border  security. 
Lastly,  the  recently  proposed  AI  Act  will  facilitate  the 
formalisation of the AI ecosystem by introducing best 
practices, however, it should be balanced in order not 
to stifle innovation and performance.

FUJITSU – Palm Secure: Use Your Hand to Unlock the World

Mr Pieter Joris, Distribution Account Manager at Fujitsu Belgium

Mr  Joris  presented  Fujitsu’s  biometric  recognition 
technology PalmSecure which is based on palm vein 
recognition.  He  opened  his  presentation  by  outlining 
five  key  requirements  determining  the  usability  of 
biometric  recognition  modality.  First,  it  should  be 
universal in the sense that every person possesses this 
trait.  Second,  it  should  enable  reliable  differentiation 
between  different  individuals.  Third,  the  quality  of 

modality  in  terms  of  performance  (i.e.  recognition) 
should  be  measurable.  Fourth,  it  should  not  change 
significantly over time. Last, in order to be practical, it 
should also allow for fast operation. He continued with 
an overview of the technical solution behind the palm-
vein  recognition  technology.  The  rest  of  the  process 
is  common  to  other  biometric  modalities,  meaning 
that  after  the  image  of  palm  veins  is  captured  using 

Industry Roundtable November 2021 Reporteulisaroundtable.eu26

infrared  imaging,  a  template  is  stored  and  converted 
into a hash hey, which can then be stored on a device 
(e.g. a smart card).

There  are  several  advantages  to  using  palm  vein 
identification. First, it offers high levels of security as 
it  is  hidden  under  the  skin,  it  is  unique  (even  in  case 
of identical twins), it doesn’t change over time, and it 
allows for simple liveness detection. Second, it is very 
precise due to the complexity of palm vein structure, 
with  more  than  five  million  reference  points,  and 
since palm veins are significantly thicker than fingers, 
making them more easily identifiable. Lastly, it offers a 
number of usability benefits, such as contactless and 
intuitive operation, as well as high level of privacy due 
to  the  fact  that  palm  vein  image  cannot  be  captured 
without the use of IR imaging.

In the context of border control, by anchoring identity 
in biometrics, palm vein could be used in combination 
with  other  biometric  modalities,  complementing 

other  modalities  with  such  benefits  as  impossibility 
of  loss  of  the  identifier,  as  well  as  impossibility  of 
spoofing  or  modification.  A  PoC  is  currently  ongoing 
at  the  European  Parliament,  using  palm  vein  for 
authentication in access control.

Mr  Joris  also  presented  a  scenario  where  palm  vein 
technology  was  used  to  secure  access  to  a  football 
stadium, which presents challenges that are similar to 
border crossing processes, such as the need for fast 
processing, convenience and ease of use, low FRR and 
lowest possible FAR, no impact of lighting conditions, 
and possibility to integrate with other systems. In the 
specific use case presented, the football club has over 
180  thousand  members.  Despite  the  relatively  high 
number  of  enrolled  members,  the  system  maintains 
very  high  quality  and  speed  of  verification,  although 
this figure is clearly significantly lower than in case of 
border-crossing scenarios.

Mobai – Industry Perspective on Face Morphing 
and Mobile ID Checks

Mr Brage Strand, CEO of Mobai
Prof. Raghavendra Ramachandra, Chief Scientist

Opening the presentation, Mr Strand posed a question: 
what if we couldn’t trust passports anymore?

Prof.  Ramachandra  continued  with  an  explanation 
of  what  face  morphing  is  and  how  it  can  be  used  in 
border-crossing  scenarios.  He  further  explained  that 
different types of image modification can have different 
effects,  for  example,  sharpening  of  the  image  due  to 
changing  lighting  might  not  affect  the  performance 
of  an  automated  facial  recognition  system  or  even 
manual  recognition,  whereas  morphing  the  images 
of  several  individuals  may  have  a  significant  effect. 
By  morphing  the  images  of  two  individuals,  a  single 
identity  document  can  be  used  by  both  individuals, 
as  the  facial  recognition  system  may  verify  both 
individuals  as  a  match  to  the  photo  provided  in 
the  identity  document.  This  is  possible  due  to  the 
vulnerabilities in the passport issuance process, which 
accepts printed images with passport applications, or 
uploading images via an online application system, as 
is the case in the UK.

There  are  two  common  approaches  to  morphing 
attack  detection  (MAD):  single 
image  MAD  and 
differential MAD. In single-image MAD, different types 
of  algorithms  are  applied  to  evaluate  whether  the 
passport  image  it  is  a  bona  fide  image  or  a  morph. 

In  differential  MAD,  different  algorithms  are  applied 
to analyse two images: one captured using a trusted 
device (e.g. ABC gate), and another image stored on a 
passport chip.

Prof.  Ramachandra  went  on  to  introduce  a  study, 
where  experts  and  non-experts  in  facial  recognition 
were  asked  to  identify  morphed  images.  The  study 
demonstrated that even experts specialised in manual 
examination and face comparison could identify only 
72.56%  of  morphed  images  in  case  of  differential 
MAD, and 64.63% in case of single MAD.

In  concluding  the  presentation,  Mr  Strand  provided 
an  overview  of  different  methods  for  tackling  MAD. 
First,  enrolment  using  a  trusted  device.  Second, 
training  people  working  with  manual  examination  of 
documents. Third, using automatic morphing detection 
systems, with ABC gates as the most obvious places 
for  introducing  such  systems.  Similarly,  morphing 
detection  can  be  implemented  in  a  differential  MAD 
scenario, when a picture made by a third party is used. 
In  this  case  differential  MAD  can  be  applied  to  an 
image provided by the applicant and the image taken 
using  a  trusted  device  when  picking  up  a  passport, 
significantly improving morphing attack detection.

Industry Roundtable November 2021 Reporteulisaroundtable.eusecunet Security Networks – Morphing Attack 
Detection in Practice

Mr Michael Schweiger, Senior Product Manager, Homeland Security Division

27

A  morphing  attack  is  when  an  identity  document  is 
created using a morphed image, which can then be used 
by  two  individuals  for  travel  purposes  with  a  significant 
likelihood that it will be verified by automated recognition 
solutions, for example, an ABC gate. Mr Schwaiger went 
on to explain that it is not simply a theoretical problem, 
but  something  that  national  authorities  have  to  deal 
with  on  a  daily  basis.  He  elaborated  on  this  point  using 
an example from Slovenia, where they have detected 40 
morphed images, all of them related to organised crime.

Similar  to  the  previous  presentation  by  Mobai,  Mr 
Schweiger  suggested  three  countermeasures  to  tackle 
MAD. First, opting for live enrolment when issuing identity 
documents. This is especially problematic in case of TCNs 
crossing Schengen borders using automated systems in 
the  context  of  the  EES.  Second,  training  human  experts 
to perform morphing attack detection, which may have a 
significant positive effect on the performance of human 
experts. Third, using AI in automated MAD.

Mr  Schweiger  continued  his  presentation  with  an 
explanation  of  how  MAD  is  implemented  in  the  ABC 
gate  solution  provided  by  secunet.  In  this  particular 
implementation,  border  control  officers  have  a  review 
screen where they can see what is happening at each of 
the gates, including the verification of travel documents, 
checking  of  facial  biometrics  against  the  image  stored 

on  the  passport  chip,  presentation  attack  detection 
and  morphing  attack  detection.  Presenting  the  results 
of  testing  the  algorithm  in  operation,  Mr  Schweiger 
suggested  that  the  algorithm  is  suitable  for  daily  use, 
as  at  the  threshold  of  2%  for  FPR,  the  system  is  able 
to  detect  85-88%  of  available  morphs.  In  terms  of  error 
rates,  this  means  Attack  Presentation  Classification 
Error Rate (APCER) of 12-15% at Bona Fide Presentation 
Classification Error Rate (BPCER) of 2%. The 2% BPCER 
can be considered as optimal in border control scenarios, 
allowing  to  identify  up  to  88%  of  morphs  already  at  the 
level of ABC gates.

Closing his presentation Mr Schweiger outlined the future 
outlook for MAD. First, automated MAD will be the reality 
quite soon. Nevertheless, detecting high quality morphs 
remains a challenge. Another objective is to continue re-
training the algorithms to further reduce the APCER rate 
at  realistic  target  BPCER  rate  for  operational  settings. 
In  this  regard  two  challenges  remain  to  be  addressed: 
frontal images and face masks. MAD systems can also 
be further improved by incorporating learning from high-
performing  human  experts,  as  well  as  using  different 
methods  for  generating  morphs  that  can  be  used  for 
algorithm training. Last but not least, automated MAD is 
only one part of the story, which needs to be accompanied 
by live enrolment and training of human experts to ensure 
highest level protection against morphs.

Industry Roundtable November 2021 Reporteulisaroundtable.euPanel Discussion and Q&A

28

The 
panel 
concluding 
discussion  focusing  on  AI 
in  biometric 
recognition 
technologies was led by Dr 
Ramon  Blanco,  IT  Officer 
at  eu-LISA’s  Planning  and 
Standards Unit.

First  question  addressed 
to  IDEMIA  focused  on  the 
social  acceptability  of 
in 
biometric 
the  context  of  CCTV,  in  particular  making  sure  that 
faces are not recorded.

recognition 

Responding  to  this  question,  Mr  Bouatou  explained 
that  the  solution  presented  is  in  development  stage 
and  is  not  yet  in  active  use.  He  suggested,  however, 
that this issue should be addressed at policy level, in 
particular  by  defining  requirements  for  implementing 
is 
anonymization.  For  example, 
implemented  at  camera  level,  then  there  should  be 
little concern that anyone could get to un-anonymized 
data  at  any  point.  Another  way  to  address  this 
issue  would  be  to  encrypt  the  original  video  after 
anonymization,  and  in  this  case  regulations  should 
define  access  to  decryption.  The  main  takeaway  is 
that with the introduction of AI, we will have not only 
better-performing  biometric  technologies,  but  also 
more privacy-preserving biometric technologies.

if  anonymization 

The  second  question  inquired  about  the  tools  used 
by criminals to create morphed images. According to 
Mr Strand (Mobai) there are a number of open source 
tools available, including openCV.

Prof.  Ramachandra  added  that  the  choice  of  tools 
depends on the technical competency of the criminals. 
For example, less technically advanced criminals use 
open  source  tools,  while  more  advanced  criminals 
use  deep 
learning-based  approaches,  which  are 
also  available  as  open  source.  When  it  comes  to 
presentation  attacks  in  palm-vein  biometrics,  they 
are  difficult  to  orchestrate  because  replicating  vein 
patterns is technically not feasible.

The third question focused on IDEMIA’s R&D work on 
the use of biometrics in other domains, such as gesture 
recognition or recognition of symptoms of illnesses. 

Mr Bouatou (IDEMIA) explained that IDEMIA’s research 
focuses  on  biometric  applications  in  video  analytics. 
He stressed that IDEMIA is not a health company, and 
it  is  not  working  on  healthcare  related  applications. 
However,  when 
it  comes  to  gesture  recognition, 
IDEMIA’s  video  analytics  research  is  focusing  on 
identifying  what  is  happening  in  a  video  stream.  Mr 
Strand  (Mobai)  followed  up  on  this  question,  zeroing 
in the identification of malicious intent. He argued that 
the problem of lie detection is an extremely challenging 

problem for AI, and we should be extremely cautious 
when  developing  and  deploying  such  solutions.  Mr 
Strand (Mobai) expanded on that, pointing out a wide 
range of cultural idiosyncrasies when it comes to lying, 
e.g., movement and speech patterns, etc. All of those 
elements  are  relevant  from  an  investigator’s  point 
of  view,  making  it  extremely  hard  to  capture  with  an 
automatic system trained in gesture analysis.

The next question inquired about the future outlook in 
the  development  and  application  of  AI  in  the  area  of 
biometric technologies.

The  representative  from  Fujitsu  proposed  that  the 
main  challenge  for  PalmSecure  is  the  high  number 
of  identifiers  against  which  an  individual  would  need 
to  be  identified  in  case  of  one-to-many  scenario. 
Here  the  challenge  is  to  ensure  similarly  low  level  of 
False  Acceptance  Rate.  Mr  Schwaiger  (secunet),  in 
turn,  pointed  out  that  behavioural  analytics  is  one  of 
the  more  challenging  areas.  For  example,  a  person 
may  exhibit  signs  of  nervousness  because  they  are 
traveling with a fake passport, but it may also be due 
to  the  fact  that  they  are  using  an  e-gate  for  the  first 
time. This is why secunet is focusing on core problems 
such as MAD or PAD, where there is ground truth and it 
is possible to develop well-performing algorithms with 
reliable results.

Another  question  from  the  audience  focused  on  the 
persistent  biases  in  biometric  systems,  as  proven  by 
scientists, related to race or gender.

Mr Bouatou responded that one needs to be very clear 
about  defining  specific  applications  where  bias  has 
been identified, and bear in mind that not all of these 
applications  are  biometric  or  facial  recognition.  For 
example, in the recent evaluation performed by NIST, 
it  was  shown  that  error  rates  due  to  gender  or  race 
are  extremely  small,  as  it  is  affected  by  the  size  of 
the dataset against which identification is performed, 
as  well  as  whether  the  results  of  the  algorithms  are 
calibrated,  which  can  also  address  the  issue  of  bias, 
including in real time. The NIST evaluation concluded 
that  there  is  hardly  any  measurable  difference  in  the 
performance of facial recognition algorithms based on 
gender or race.

The  final  question  in  the  discussion  focused  on 
presentation  attack  detection  (PAD)  in  contactless 
fingerprint scanners.

According  to  Mr  Bouatou  this  is  a  problem  that  is 
much  harder  to  solve.  IDEMIA  has  been  working  on 
contactless fingerprint technology for 10 years, and PAD 
algorithms built into the technology are able to detect 
most presentation attacks. However, PAD in a scenario 
when fingerprint capture is being performed using an 
unknown  device  in  an  unsupervised  environment,  is 
very  difficult  to  control.  In  general,  PAD  is  strong  in 

Industry Roundtable November 2021 Reporteulisaroundtable.eucase  of  trusted  devices  with  known  illumination.  Of 
course, this doesn’t mean that mobile devices cannot 
be  used  because  a  trustworthy  operator  operating 
a  mobile  device  for  remote  contactless  fingerprint 
acquisition  can  work  just  fine.  In  such  cases,  there’s 
no  reason  to  worry  about  PAD  because  the  operator 

would notice if there is an issue. However, otherwise it 
is not possible to say that there’s a bulletproof solution 
for PAD in contactless enrolment.

29

Industry Roundtable November 2021 Reporteulisaroundtable.euClosing Remarks

Mr Luca Tagliaretti, Deputy Executive Director, eu-LISA

30

this 

eu-LISA’s 

that  eu-LISA 

iteration 
In  closing 
Industry 
of 
Roundtable,  Mr  Tagliaretti 
argued 
is 
tackling challenges that are 
important not only in Europe 
but on a global scale. These 
are  challenges  that  none 
of  us  can  resolve  alone, 
which is why public-private 
partnership is the best way 
forward.  In  that  sense,  eu-LISA’s  Industry  Roundtable 
offers  a  bridge  to  new 
ideas  and  technologies, 
suggesting avenues for new research and approaches. 
Therefore,  research  and  development  are  one  of  the 

key  priorities  for  the  Agency.  Mr  Tagliaretti  went  on 
to  underscore  that  AI  is  an  extremely  topical  issue 
for  eu-LISA  and  its  stakeholders,  especially  in  light 
of  the  proposed  EU  Artificial  Intelligence  Act.  The 
Agency’s  position  on  this  issue  is  clear:  we  should 
not  regulate  AI  as  such,  instead  we  should  regulate 
specific  applications  of  this  technology,  bearing  in 
mind the public interest and our mission. Therefore, it 
is important to keep discussing these issues to further 
our understanding, as we did during this event, and this 
is why events like the eu-LISA Industry Roundtable are 
very  important  for  raising  awareness  and  increasing 
trust,  helping  us  in  our  work  towards  our  common 
objectives.

Industry Roundtable November 2021 Reporteulisaroundtable.euCatalogue number: EL-AN-21-002-EN-N
ISBN 978-92-95217-97-3
ISSN: 2600-2728
DOI:10.2857/870875

© European Union Agency for the
Operational Management of Large-Scale IT Systems 
in the Area of Freedom, Security and Justice, 2022