biometric technologies and behavioral security

Pattern Recognitionand Applications Lab

Università degli Studidi Cagliari, Italia

Dipartimento diIngegneria Elettrica

ed Elettronica

Biometric technologiesand behavioral security

Gian Luca Marcialis

marcialis@unica.it

https://people.unica.it/gianlucamarcialis/

M.Sc. Degree In Computer Engineering, CyberSecurity and Artificial Intelligence

http://pralab.diee.unica.it

Biometrics @ PRA Lab

http://pralab.diee.unica.it/en/Biometrics

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http://pralab.diee.unica.it

Overview of the course

• Biometric technologies– Definition

– Fingerprints

– Faces

– Multiple biometrics

– Other biometrics

• Security and biometrics– Vulnerabilities of biometric systems

– Presentation Attacks Detection

– Design of countermeasures against spoofing attacks

• Behavioural security– Crowd analysis

– Anomalous behaviour

– Human-in-the-loop

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http://pralab.diee.unica.it

Course organization

• Lectures and laboratory exercises– Monday, 9am, LIDIA room (2 hours)

– Thursday, 8am, LIDIA room (3 hours)

• Balance between lectures and exercises

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Lectures58%

Exercises42%

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What will you learn during lectures?

• Basics of biometric systems design and evaluation

• Main modules of a biometric system

• How pattern recognition and machine learning concepts are applied to biometric recognition

• Design of presentation attacks detectors as a basic protection«infrastructures» for biometric systems

• How people behavior can be «detected» for mass-oriented video-surveillance systems

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What will you learn to do during the laboratory exercises?

• To design and test biometric recognition systems

• Using advanced programming languages and tools for biometricapplications– Matlab/Python/C

– Image processing

– Pattern recognition

– Deep learning

• Exploring pros and cons of video-based analysis

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http://pralab.diee.unica.it

How to gain the exam score?

• By following the course

– Last 5 hours of laboratory exercises will be a true biometric system design test

– This can be carried out alone or by group of two/three people maximum

• By the M.Sc. Thesis

– In this case, it is not necessary to develop anything more than a good topic, under my supervision

• By an oral verification on the whole course program

– 60/90 minutes of interview with me and my co-workers

– Cross your fingers and good luck!

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How to study

• Follow the course

• Do questions– During lecture/lab exercises

– During the break

– By e-mail

• Download the course slides– https://people.unica.it/gianlucamarcialis/

• Integrate your notes and book information (no wikipedia)

• Do programming exercises at home

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http://pralab.diee.unica.it

Bibliography

• A. Jain et al., Handbook of Biometrics, Springer

• B. Bhanu and A. Kumar, Deep learning in biometrics, Springer

• K. Saeed, New direction in behavioural biometrics, CRC Press

• V. Murino et al., Group and crowd behavior for computer vision, Academic Press

• D. Maltoni et al., Handbook of fingerprint recognition, Springer

• H. Liu, Face Detection and Recognition on Mobile Devices, Elsevier

• M. Vatsa et al., Deep learning in biometrics, CRC Press

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Use-inspired research

Did you seesomething?

No, it’s allright!

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Vulnerability points

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Fingerprint anti-spoofing

Stand-alone fingerprintverification systemwith anti-spoofing

tailored for differentcapture devices

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«Genuine» spoofs from our lab

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What about… deep fakes?

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Behavioural security: starting point

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Cluster detection

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A recent project: the BullyBuster

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Bari – Cagliari – Foggia – Napoli «Federico II»

http://pralab.diee.unica.it

Let’s start:the biometric paradigm

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Definition

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Something you are

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Something that (only) you (should) know

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Identity theft and cybersecurity

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Biometric systems and «security chain»

• Identity check is the first stage in current «security chain»

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Biometric properties

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Biometrics for personal recognition

• Biometric systems are pattern recognition-based algorithms

• They require that the system is «trained»

– By collecting a set of the target users «templates»

– By embedding the uniqueness measurements of users as system’s parameters

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Biometricsystem

Who is he?Is he who isclaiming to

be?

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Biometric system’s rough view

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Feature

extraction Identification?

Verification

module

Identification

module

yes

no

Score or

distance set

Score or

distance

Identification module

Classification

module

Fingerprints

Data Base

Matching

module

Verification module

Fingerprints

Data Base

Matching

module

Claimed Identity

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Personal recognition…

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AirportSUSPECT

Comparison with

a lot of possible

targets

Suspect subjects database

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…and personal verification

DATABASE of Templates

MATCHERFEATURE

EXTRACTOR

I verified this is

YOUR fingerprint

ACCESS GRANTED

This is my fingerprint –please grant me access to

my home

http://pralab.diee.unica.it

That’s all for today

• Pattern Recognition: what is it?– Pre-processing

– «Features» extraction

– Machine learning

– Classification

• Some recalls about statistics– Random variables

– Distributions

– Hypothesis verification tests

• Don’t miss the next exciting lecture!

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Thank you for listening!

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Gian Luca Marcialis

Phone: +39 070 675 5893E-mail: marcialis@unica.itWeb: http://pralab.diee.unica.it

Università degli Studi di CagliariDip. Ing. Elettrica ed Elettronica