The document provides an overview of biometrics, including: - Defining biometrics as using physical/behavioral attributes to authenticate individuals - Describing the history and development of biometrics from ancient uses to modern automated systems - Explaining the categories, components, and performance metrics of biometric systems - Detailing specific biometric technologies like fingerprints, iris scans, facial recognition, and their applications - Predicting future trends like standards development and passive facial recognition for surveillance

1. - MAJ PRAVEEN TRIPATHI
A -SYN

2. AIM
TO ACQUAINT THE CLASS WITH THE
CONCEPT AND APPLICATION OF
BIOMETRICS.

3. PHASES
PHASE I INTRODUCTION
PHASE II HISTORY OF BIOMETRICS
PHASE III CATEGORIES OF BIOMETRICS
PHASE IV VARIOUS BIOMETRIC SYSTEMS
PHASE V FUTURE TRENDS

4. PHASE I
INTRODUCTION

5.  WHAT IS BIOMETRICS?
GREEK “BIO” AND “METRICS”
 BIOMETRICS IS A TECHNOLOGY THAT INVOLVES
AUTHENTICATION TECHNIQUES OF INDIVIDUALS
 BASED ON THEIR PERSONAL OR PHYSICAL ATTRIBUTES.
 STRONG OR TWO-FACTOR AUTHENTICATION IS
BECOMING A DE FACTO STANDARD IN SECURE
COMPUTING ENVIRONMENT.

6.  STRONG AUTHENTICATION CAN BE COUPLED WITH
PIN , PASSWORD OR A SMART CARD.
 BY REPLACING PINs , BIOMETRIC TECHNIQUES CAN
PREVENT UNAUTHORIZED ACCESS TO :
 ATMs
 CELL PHONES
 SMART CARDS
 DESKTOP PCs
 WORKSTATIONS

7. PHASE II
HISTORY OF BIOMETRICS

8.  14TH CENTURY : MERCHANTS IN CHINA USED
CHILDREN’S PALM AND FOOTPRINTS TO DISTINGUISH
THEM FROM ONE ANOTHER.
 19TH CENTURY : ALPHOSE BERTILLON A POLICE CLERK
IN PARIS INVENTED ANTHROPOMETRY . THE SYSTEM
WAS CALLED BERTILLONAGE.
 LATE 19TH CENTURY: FINGERPRINTING STARTED BY
ANTHROPOLOGIST SIR FRANCIS GALTON.
 LATE 20TH CENTURY : AUTOMATED BIOMETRIC
TECHNIQUES STARTED
1993 - FIRST IRIS IDENTIFICATION SYSTEM.
1994 - FIRST IRIS RECOGNITION ALGORITHM WAS
PATENTED

9. PHASE III
CATEGORIES OF BIOMETRICS

11.  TWO MODES OF BIOMETRIC SYSTEMS:
VERIFICATION MODE – ONE-TO-ONE COMPARISON OF
CAPTURED BIOMETRIC WITH THE SPECIFIC
TEMPLATE.
IDENTIFICATION MODE – ONE-TO-MANY
COMPARISON AGAINST A BIOMETRIC DATABASE TO
ESTABLISH THE IDENTITY OF AN UNKNOWN
INDIVIDUAL.
 POSITIVE AND NEGATIVE RECOGNITION : NEGATIVE
RECOGNITION USED GENERALLY IN FORENSICS.

12. BIOMETRIC SYSTEM: BASIC BLOCK DIAGRAM

13.  ENROLLMENT WHEN SYSTEM IS USED FOR THE FIRST
TIME.
 SENSOR THE INTERFACE BETWEEN THE REAL WORLD &
SYSTEM.
 PRE-PROCESSING ENHANCES THE INPUT BY REMOVING
BACKGROUND NOISE, REMOVING ARTIFACTS FROM THE
SENSOR.
 TEMPLATE GENERATOR GENERATES A TEMPLATE FROM
THE SOURCE.
 MATCHER USES MATCHING ALGORITHM TO COMPARE
TEMPLATES.

14. PERFORMANCE METRICS
 EXTREMELY SENSITIVE – MAY CAUSE FALSE POSITIVES &
FALSE NEGATIVES.
 SHOULD BE CALIBRATED TO ENSURE ACCURACY.
 FALSE ACCEPT RATE OR FALSE MATCH RATE (FAR/FMR)
PROBABILITY OF INCORRECTLY MATCHING THE INPUT
PATTERN TO NON-MATCHING TEMPLATE IN DATABASE.
TYPE II ERRORS MOST DANGEROUS
 FALSE REJECT RATE OR FALSE NON-MATCH
RATE(FRR/FNMR) PROBABILITY OF FAILING TO MATCH
BETWEEN THE INPUT PATTERN & A MATCHING TEMPLATE
IN DATABASE.
TYPE I ERRORS

15.  RELATIVE OPERATING CHARACTERISTIC(ROC) CURVE IS
A VIRTUAL CHARACTERIZATION BETWEEN THE FAR &
FRR.
 MATCHING ALGORITCISION GIVES DECISION BASED ON A
THRESHOLD.
 THRESHOLD DIRECTLY PROPORTIONAL TO FRR &
INVERSELY PROPORTIONAL TO FAR.
 CROSSOVER ERROR RATE(CER)
-RATE AT WHICH BOTH ERRORS ARE EQUAL.
-OBTAINED BY ROC CURVE.
-DEVICE WITH LOWEST CER IS THE MOST ACCURATE.

16.  FAILURE TO ENROLL RATE(FTR) RATE AT WHICH
ATTEMPTS TO CREATE A TEMPLATE FROM AN INPUT ARE
UNSUCCESSFUL.
 FAILURE TO CAPTURE RATE(FTC) PROBABILITY THAT THE
SYSTEM FAILS TO DETECT A BIOMETRIC INPUT.
 TEMPLATE CAPACITY MAX NUMBER OF SETS OF DATA
WHICH CAN BE STORED IN THE SYSTEM.

17. PHASE IV
VARIOUS BIOMETRIC SYSTEMS

18. FINGERPRINT
 HUMAN FINGERPRINTS ARE
MADE UP OF RIDGE ENDINGS
& BIFURCATIONS & OTHER
DETAILED CHARACTERISTICS
CALLED MINUTIAE.
 HAS AN EDGE OVER OTHER
ELECTRONIC ACCESS
CONTROL DEVICES.
 BEING INCORPORATED IN DAY TO DAY LIFE LIKE
LAPTOPS, CELL PHONES, CARS & HOUSE/HOTEL
SECURITY.

19.  BIOMETRIC FINGERPRINTING IS DIFFERENT FROM THAT
OF FORENSICS AS IT ONLY EXTRACTS SPECIFIC
FEATURES FOR LESSER HARD DRIVE SPACE &
QUICKER DATABASE LOOKUPS.
PALM SCAN
 PALM SCANNER RECOGNITION SYSTEMS MEASURE &
ANALYZE THE OVERALL STRUCTURE, SHAPE &
PROPORTIONS OF THE HAND.
 IT INCLUDES THE FIVE FINGERPRINTS ALSO.
 CHARACTERISTICS OF SKIN SURFACE.

20. HAND GEOMETRY
 SHAPE OF A PERSON’S HAND DEFINES HAND GEOMETRY.
 PERSON PLACES HAND ON A DEVICE THAT HAS
GROOVES FOR EACH FINGER.
 SYSTEM COMPARES THE GEOMETRY OF EACH FINGER &
THE HAND AS A WHOLE TO THE INFORMATION IN A
REFERENCE FILE TO VERIFY THE PERSONS IDENTITY.

21. RETINA SCAN
 DUE TO COMPLEX STRUCTURE OF BLOOD SUPPLYING
CAPILLARIES, EACH PERSONS RETINA IS UNIQUE.
 RETINA REMAINS UNCHANGED FROM BIRTH TILL DEATH.
 THE BLOOD VESSELS WITHIN RETINA ABSORB LIGHT
MORE READILY THAN THE SURROUNDING TISSUE & ARE
EASILY IDENTIFIED WITH APPROPRIATE LIGHTING.

22. IRIS SCAN
 AUTOMATED METHOD OF BIOMETRIC IDENTIFICATION THAT
USES MATHEMATICAL PATTERN- RECOGNITION TECHNIQUES
ON VIDEO IMAGES OF THE IRISES OF AN INDIVIDUAL’S EYES.
 UNLIKE RETINAL SCAN,
IRIS RECOGNITION USES
CAMERA TECHNOLOGY
WITH INFRARED ILLUM
TO ACQUIRE IMAGES OF
THE DETAIL RICH, INTRICATE
STRUCTURES OF THE IRIS.
 DIGITAL TEMPLATES ENCODED FROM THESE PATTERNS BY
MATHEMATICAL & STATISTICAL ALGORITHMS ALLOW
UNAMBIGUOUS POSITIVE IDENTIFICATION OF AN INDIVIDUAL.

23.  THE KEY ADVANTAGE OF IRIS RECOGNITION IS THE STABILITY OF
THE IRIS AS AN INTERNAL , PROTECTED, YET EXTERNALLY VISIBLE
ORGAN OF THE EYE.
 THE CORE ALGORITHMS THAT UNDERLIE IRIS RECOGNITION WERE
DEVELOPED IN THE 1990s BY PROF JOHN DOUGMAN AT UNIVERSITY
OF CAMBRIDGE COMPUTER LAB.
 AS OF 2008, DAUGMAN`S ALGORITHMS ARE THE BASIS OF ALL
COMMERCIALLY DEVELOPED IRIS RECOG SYSTEMS.

24. FACIAL SCAN
 THIS TECH LOOKS AT SPECIFIC
PARTS OF THE FACE THAT DO NOT
CHANGE SIGNIFICANTLY OVER TIME,
SUCH AS
- UPPER SECTION OF EYE SOCKETS
- AREA SURROUNDING CHEEK
BONES.
- SIDES OF MOUTH.
- DISTANCE BETWEEN THE EYES.
 A COLLECTION OF FACE IMAGES IS USED TO GENERATE
A 2D GRAY-SCALE IMAGE TO PRODUCE A BIOMETRIC
TEMPLATE.

25.  HIGHLY SENSITIVE SECURITY ENVIRONMENT , SEVERAL
CAMERAS AT DIFFERENT ANGLES SHOULD BE USED TO
PRODUCE AN EXACT SAMPLE.
 TEST FOR LIVENESS- BLINKING OF EYES.
 FACIAL RECOGNITION IS GENERALLY SUBJECT TO
LARGER MARGINS OF ERROR THAN MORE ESTABLISHED
BIOMETRICS SUCH AS FINGERPRINT RECOG.

26. SIGNATURE DYNAMICS
 DYNAMIC SIGNATURE CAPTURES DISTINCT
BEHAVIOURAL CHARACTERISTICS OF AN INDIVIDUAL`S
SIGNATURE.
 INCL SHAPE, SPEED, STROKE, PEN PRESSURE & TIMING
INFORMATION.
 CONSISTS OF A PEN & A SPECIALIZED WRITING TABLET &
ARE CONNECTED TO A LOCAL OR CENTRAL COMPUTER
FOR TEMPLATE PROCESSING & VERIFICATION.
 INDL MUST SIGN THEIR NAME MULTIPLE TIMES ON THE
TABLET.
 SYSTEM EXTRACTS UNIQUE FEATURES FROM IT LIKE
THE TIME UTILIZED, PRESSURE APPLIED FROM THE PEN,
THE SPEED, OVERALL SIZE & VARIOUS DIRECTIONS OF
STROKES IN THE SIGNATURE.

27. KEYSTROKE DYANAMICS
 THE KEYSTROKE RHYTHMS OF A USER ARE MEASURED
TO DEVELOPED A UNIQUE BIOMETRIC TEMPLATE OF THE
USERS TYPING PATTERN FOR FUTURE AUTHENTICATION.
 SYSTEM DETERMINES DWELL TIME (THE TIME A KEY
PRESSED) & FLIGHT TIME (TIME BETWEEN KEY UP & THE
NEXT KEY DOWN)
 THE RECORDED KEYSTROKE TIMING DATA IS THEN
PROCESSED THROUGH A UNIQUE ALGORITHM, WHICH
DETERMINES A PRIMARY PATTERN FOR FUTURE
COMPARISON.

28. VOICE PRINT
 OUR VOICE IS UNIQUE BECAUSE OF THE SHAPE OF OUR
VOCAL CAVITIES & WAY WE MOVE OUR MOUTH WHILE
SPEAKING.
 TO ENROLL IN A VOICE PRINT SYS ONE SHOULD SAY THE
EXACT WORDS AND PHRASES THAT IT REQUIRES.
 DATA USED IN A VOICE PRINT IS A SOUND
SPECTROGRAM.
 A SPECTROGRAM IS BASICALLY A GRAPH THAT SHOWS A
SOUND’S FREQUENCY ON THE VERTICAL AXIS & TIME ON
THE HORIZONTAL AXIS.
 DIFFERENT SPEECH SOUNDS CREATE DIFFERENT
SHAPES WITHIN THE GRAPH.
 SPECTROGRAMS ALSO USE COLORS OR SHADES TO
REPRESENT THE ACOUSTICAL QUALITIES OF SOUND.

29. PHASE V
FUTURE TRENDS

30.  THE FUTURE OF BIOMETRICS HOLDS GREAT PROMISE
FOR LAW ENFORCEMENT APPLICATIONS.
 IT IS QUICKLY BECOMING RECOGNIZED AS THE MOST
ACCURATE IDENTIFICATION TECHNOLOGY IN THE
MARKET.
 ACCESS CONTROL FACIAL RECOGNITION
BIOMETRIC TECHNOLOGIES WILL PERMIT AUTH USERS
ENTRY TO A PROPERTY .
IN FUTURE THE SUBJECT WILL NOT HAVE TO STAND
NEAR THE SURVEILLANCE CAMERAS.
 FACIAL RECOGNITION PASSIVE SURVEILLANCE HIDDEN
SURVEILLANCE CAMS WILL BE SET TO MONITOR AN AREA
TO ACCURATELY IDENTIFY A SUSPECT AGAINST A
DATABASE OF MILLIONS IN A SECOND.

31.  ALERT MANAGEMENT THIS IS A FULLY CUSTOMISABLE
COMMAND CENTER TO GUARD AGAINST POTENTIAL
SECURITY BREECHES.
 CENTER USES REAL-TIME TECHNOLOGIES TO DELIVER
SECURITY ALERTS TO MULTIPLE LOCATIONS THROUGH
PDA AND OTHER MOBILE TECHNOLOGIES.
 FOR BIOMETRICS FIELD TO GROW INDUSTRY
STANDARDS MUST EXIST SO THAT THERE IS THE
GREATEST COMPATIBILITY BETWEEN APPLICATIONS AND
HARDWARE.

32. CONCLUSION
BIOMETRICS IS THOUGH EXPENSIVE IS A
TECHNOLOGY THAT CAN SIMPLIFY THE PROCESS
OF AUTHENTICTION. THE WIDE VARIETY OF
PHYSICALLY UNIQUE TRAITS GOD HAS GIVEN US
WILL SOON ALLOW US TO LIVE IN A VERY SECURE
PASSWORD-LESS WORLD.

33. ANY QUESTIONS ???