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Slide1

Subtitle

2015 GenCyber Cybersecurity Workshop

An

Overview of

Biometrics

Dr. Charles C. Tappert

Seidenberg School of CSIS, Pace University

http://csis.pace.edu/~ctappert

/

Slide2

Biometrics Information Sources

Most of the images and material contained here are from: Guide to Biometrics Bolle, et al., Springer 2004And our conference, journal, and book publications, see http://www.csis.pace.edu/~ctappert/tappert/pubs.htm

Slide3

What is Biometrics?

The

science of identifying, or verifying the identity of, a person based on physiological or behavioral

characteristics/traits

Physical traits

Fingerprint, Face, Iris

Behavioral traits

Signature/handwriting, Voice

Keyboard and mouse input

Websites and videos

http://www.biometrics.gov/

Biometric Security

Slide4

Technologies Used in Biometrics

Pattern Recognition

(

PhD Course

,

JPR

)

Machine Learning

Artificial Intelligence

Data Mining

Beer and Diapers

Target Figured Out A Teen Girl Was Pregnant Before Her Father Did

Slide5

Pattern RecognitionWhat is pattern recognition?

The act of taking in raw data and taking an action based on the “category” of the pattern

We gain an understanding and appreciation for pattern recognition in the real world – visual scenes, noises, etc.

Human senses: sight, hearing, taste, smell, touch

Recognition not an exact match like a password

Slide6

Pattern RecognitionAn Introductory Example(from Pattern Classification by Duda, et al.)

Sorting incoming Fish on a conveyor according to species using optical sensing Sea bass Species Salmon

Slide7

Set up a camera and take some sample images to extract featuresLengthLightnessWidthNumber and shape of finsPosition of the mouth, etc…

Pattern Recognition

Problem Analysis

Slide8

PreprocessingSegment (isolate) fishes from one another and from the backgroundFeature ExtractionReduce the data by measuring certain featuresClassificationDivide the feature space into decision regions

Pattern Recognition

Pattern Classification System

Slide9

Slide10

Initially use the length of the fish as a possible feature for discrimination

Pattern Recognition

Classification

Slide11

Slide12

The length is a poor feature alone!Select the lightness as a possible feature

Pattern Recognition

Feature Selection

Slide13

Slide14

Adopt the lightness and add the width of the fish to the feature vectorFish xT = [x1, x2]

Lightness

Width

Pattern Recognition

Feature Vector

Slide15

Pattern Recognition

Straight line decision bounda

ry

Slide16

Pattern Recognition Stages

Sensing

Use of a transducer (camera or microphone)

PR system depends on the bandwidth, the resolution sensitivity distortion of the transducer

What A Drone Can See From 17,000 Feet

Preprocessing

Segmentation and grouping – patterns should be well separated and not overlap

Slide17

Feature extractionDiscriminative featuresIdeally invariant wrt translation, rotation, scaleClassificationUse the feature vector provided by a feature extractor to assign the object to a categoryPost ProcessingExploit context-dependent information to improve performance

Pattern Recognition Stages (

cont

)

Slide18

The following sentence has many spelling errors. Right click on a word to get suggested correct spelling choices.We cant allign teh wonds corektly in htis sentance.On right clicking, most of correct spellings of the words are listed as first choice.Now, type the sentence above with the spelling errors into Microsoft Word.Many of the misspelled words are almost instantaneously auto-corrected.

Pattern Recognition

Post Processing – for example, OCR

Slide19

Traditional Modes of Person Authentication

Possessions – what you have

Keys, passports, smartcards, etc.

Knowledge – what you know

Secret information: passwords, etc.

Biometrics – what you are/do

Characteristics of the human body and human actions that differentiate people from each other

Slide20

Authentication Methods:Examples and Properties

most widely used

Slide21

Most Common & Other Biometrics

Most Common

Other Biometrics

Slide22

Universality every person has the biometric characteristicUniqueness no two persons have the same biometric characteristicPermanence biometric characteristic invariant over timeCollectability measurable with a sensing deviceAcceptability user population and public in general should have no strong objections to measuring/collecting the biometric

Attributes Necessary to Make a Biometric Practical

Slide23

System performance (accuracy)Computational speed (DNA slow)Exception handling (difficult to predict)System cost (high for DNA)Security (can system be compromised?)Privacy (data confidentiality)

System Performance and Design Issues

Slide24

Identification versus Verification

Identification

1-of-n

Verification

accept/reject

Slide25

Identification versus Verification

Identification

1-of-n

Verification

accept/reject

Slide26

AcquisitionSingle 2D imageVideo sequence3D image via stereo imaging, etc.Michigan State University – Anil Jainhttp://biometrics.cse.msu.edu/Presentations/AnilJain_FaceRecognition_KU10.pdf

Face Biometric

Slide27

Each person has a unique face?

Face Recognition

Slide28

?

Query

Face DB

Face

Recognition System:

Eigenface

Algorithm

Slide29

Inspirational Portrait of Individuality

Slide30

Face Recognition: National Security

Slide31

AcquisitionInked finger impressions, scanners, etc.Problem – elastic distortionFeatures

Fingerprint Biometric

Slide32

Fingerprint Verification

Slide33

Man

Wo

man

Train

Test

Train

Test

Left

Right

Iris Authentication: Data

Slide34

Iris Authentication: Image Processing

Slide35

Biometric Authentication

A robot identifies a suspect, from the movie “Minority Report.”

Slide36

AcquisitionOffline (static information) – scanned imagesOnline (static and dynamic info) – digitizersCategories of forger sophistication Zero-effort, home-improved, over-the-shoulder, professional

Signature Biometric

Slide37

Speaker Individuality: “My name is …”

Slide38

“My name is” from Two Different Speakers

Speaker Individuality

Slide39

AcquisitionMicrophone – inexpensive, ubiquitous“My name is” divided into seven sound units

Speech Biometric – Voiceprint

Slide40

Biomouse Fingerprint scanner

Digital

Camera

LCD Pen

tablet

Microphone

Multi-modality Biometric Authentication

Embeded & Hybrid User Verification system

System that requires user verification

Slide41

Basic Authentication System Matching Errors

FAR

FRR

w = within class (same person), b = between class (different people)

Slide42

accept

reject

FAR = False Accept Rate, FRR = False Reject Rate

Basic Authentication System

Matching Errors

Slide43

Receiver Operating Characteristic (ROC) Curve

Low Security/High Convenience (liberal) can be too open

Low Convenience/High Security (conservative) can be too restrictive

FAR = False Accept Rate

Requires imposter testing

FRR = False Reject Rate

EER = Equal Error Rate

Slide44

Biometric System Evaluation Types

Technical Evaluation

Simulation tests – usual for academic studies

Scenario Evaluation

Testing facility that simulates the actual installation

Operational Evaluation

Actual installation testing – most realistic

Slide45

Typical Error Rates

Slide46

Biometric Zoo

Sheep

Dominant group, systems perform well for them

Goats

Weak distinctive traits, produce many False Rejects

Lambs

Easy to imitate, cause “passive” False Accepts

Wolves

Good at imitating, cause “active” False Accepts

Chameleons

Easy to imitate and good at imitating others

Slide47

Many Biometric Systems andInteresting Articles on the Internet

Microsoft's Age Estimator

KeyTrac

keystroke demos

:

passwords, any text

Secret Lock

Michigan State University

DNA Generated Face –

NYT science section article

Building a Face, and Case, on DNA

– March 2015

Slide48

Project Ideas

List and describe various biometrics, can you think of new ones?

What is the government doing in biometrics?

Define and describe various technologies used in biometrics

Find interesting Web and news items related to biometrics – e.g., beer and diapers, Target’s pregnant girl, DNA generated face, secret lock, age estimation

Find or go deeper into interesting technologies – e.g., spelling correction, Siri’s voice command system

List and describe the ways people use the usual authentication method of combining

what you have

and

what you know

Investigate the biometric zoo – what biometrics are easy to spoof?

Slide49

Verizon Funding – 2015 Reduce UID/Password Dependency

Most people have many UID/Passwords for access

Bank accounts, smartphone/computer, social websites, etc.

Location Component

Near Field Communication (NFC)

Near-field communication uses electromagnetic induction between two loop antennas located within each other's near field

Geofencing

Uses the global positioning system (GPS) or radio frequency identification (RFID) to define geographical boundaries

Biometrics

Explore the use of several biometrics for use in this problem area

Slide50

Copyright for Material Reuse

Copyright©

2015 Charles

Tappert

(ctappert@pace.edu),

Pace

University.

Please properly acknowledge the source for any reuse of

the materials as below.

Charles

Tappert

, 2015 GenCyber Cybersecurity Workshop, Pace University

Permission

is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation. A copy of the license is available at http://www.gnu.org/copyleft/fdl.html.

Slide51

Acknowledgment

The authors would like to acknowledge the support from the National Science Foundation under Grant No. 1027400 and from the GenCyber program in the National Security Agency. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, the National Security Agency or the U.S. government.

2015

GenCyber

Cybersecurity Workshop


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