- MAJ PRAVEEN TRIPATHI
A -SYN
AIM
TO ACQUAINT THE CLASS WITH THE
CONCEPT AND APPLICATION OF
BIOMETRICS.
PHASES
PHASE I INTRODUCTION
PHASE II HISTORY OF BIOMETRICS
PHASE III CATEGORIES OF BIOMETRICS
PHASE IV VARIOUS BIOMETRIC S...
PHASE I
INTRODUCTION
 WHAT IS BIOMETRICS?
GREEK “BIO” AND “METRICS”
 BIOMETRICS IS A TECHNOLOGY THAT INVOLVES
AUTHENTICATION TECHNIQUES OF IN...
 STRONG AUTHENTICATION CAN BE COUPLED WITH
PIN , PASSWORD OR A SMART CARD.
 BY REPLACING PINs , BIOMETRIC TECHNIQUES CAN...
PHASE II
HISTORY OF BIOMETRICS
 14TH CENTURY : MERCHANTS IN CHINA USED
CHILDREN’S PALM AND FOOTPRINTS TO DISTINGUISH
THEM FROM ONE ANOTHER.
 19TH CENTU...
PHASE III
CATEGORIES OF BIOMETRICS
 TWO MODES OF BIOMETRIC SYSTEMS:
VERIFICATION MODE – ONE-TO-ONE COMPARISON OF
CAPTURED BIOMETRIC WITH THE SPECIFIC
TEMPLA...
BIOMETRIC SYSTEM: BASIC BLOCK DIAGRAM
 ENROLLMENT WHEN SYSTEM IS USED FOR THE FIRST
TIME.
 SENSOR THE INTERFACE BETWEEN THE REAL WORLD &
SYSTEM.
 PRE-PROCESS...
PERFORMANCE METRICS
 EXTREMELY SENSITIVE – MAY CAUSE FALSE POSITIVES &
FALSE NEGATIVES.
 SHOULD BE CALIBRATED TO ENSURE ...
 RELATIVE OPERATING CHARACTERISTIC(ROC) CURVE IS
A VIRTUAL CHARACTERIZATION BETWEEN THE FAR &
FRR.
 MATCHING ALGORITCISI...
 FAILURE TO ENROLL RATE(FTR) RATE AT WHICH
ATTEMPTS TO CREATE A TEMPLATE FROM AN INPUT ARE
UNSUCCESSFUL.
 FAILURE TO CAP...
PHASE IV
VARIOUS BIOMETRIC SYSTEMS
FINGERPRINT
 HUMAN FINGERPRINTS ARE
MADE UP OF RIDGE ENDINGS
& BIFURCATIONS & OTHER
DETAILED CHARACTERISTICS
CALLED MINUT...
 BIOMETRIC FINGERPRINTING IS DIFFERENT FROM THAT
OF FORENSICS AS IT ONLY EXTRACTS SPECIFIC
FEATURES FOR LESSER HARD DRIVE...
HAND GEOMETRY
 SHAPE OF A PERSON’S HAND DEFINES HAND GEOMETRY.
 PERSON PLACES HAND ON A DEVICE THAT HAS
GROOVES FOR EACH...
RETINA SCAN
 DUE TO COMPLEX STRUCTURE OF BLOOD SUPPLYING
CAPILLARIES, EACH PERSONS RETINA IS UNIQUE.
 RETINA REMAINS UNC...
IRIS SCAN
 AUTOMATED METHOD OF BIOMETRIC IDENTIFICATION THAT
USES MATHEMATICAL PATTERN- RECOGNITION TECHNIQUES
ON VIDEO I...
 THE KEY ADVANTAGE OF IRIS RECOGNITION IS THE STABILITY OF
THE IRIS AS AN INTERNAL , PROTECTED, YET EXTERNALLY VISIBLE
OR...
FACIAL SCAN
 THIS TECH LOOKS AT SPECIFIC
PARTS OF THE FACE THAT DO NOT
CHANGE SIGNIFICANTLY OVER TIME,
SUCH AS
- UPPER SE...
 HIGHLY SENSITIVE SECURITY ENVIRONMENT , SEVERAL
CAMERAS AT DIFFERENT ANGLES SHOULD BE USED TO
PRODUCE AN EXACT SAMPLE.
...
SIGNATURE DYNAMICS
 DYNAMIC SIGNATURE CAPTURES DISTINCT
BEHAVIOURAL CHARACTERISTICS OF AN INDIVIDUAL`S
SIGNATURE.
 INCL ...
KEYSTROKE DYANAMICS
 THE KEYSTROKE RHYTHMS OF A USER ARE MEASURED
TO DEVELOPED A UNIQUE BIOMETRIC TEMPLATE OF THE
USERS T...
VOICE PRINT
 OUR VOICE IS UNIQUE BECAUSE OF THE SHAPE OF OUR
VOCAL CAVITIES & WAY WE MOVE OUR MOUTH WHILE
SPEAKING.
 TO ...
PHASE V
FUTURE TRENDS
 THE FUTURE OF BIOMETRICS HOLDS GREAT PROMISE
FOR LAW ENFORCEMENT APPLICATIONS.
 IT IS QUICKLY BECOMING RECOGNIZED AS TH...
 ALERT MANAGEMENT THIS IS A FULLY CUSTOMISABLE
COMMAND CENTER TO GUARD AGAINST POTENTIAL
SECURITY BREECHES.
 CENTER USES...
CONCLUSION
BIOMETRICS IS THOUGH EXPENSIVE IS A
TECHNOLOGY THAT CAN SIMPLIFY THE PROCESS
OF AUTHENTICTION. THE WIDE VARIETY...
ANY QUESTIONS ???
Biometrics  ... by maj praveen tripathi
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Biometrics ... by maj praveen tripathi

  1. 1. - MAJ PRAVEEN TRIPATHI A -SYN
  2. 2. AIM TO ACQUAINT THE CLASS WITH THE CONCEPT AND APPLICATION OF BIOMETRICS.
  3. 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. 4. PHASE I INTRODUCTION
  5. 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. 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. 7. PHASE II HISTORY OF BIOMETRICS
  8. 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. 9. PHASE III CATEGORIES OF BIOMETRICS
  10. 10.  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.
  11. 11. BIOMETRIC SYSTEM: BASIC BLOCK DIAGRAM
  12. 12.  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.
  13. 13. 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
  14. 14.  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.
  15. 15.  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.
  16. 16. PHASE IV VARIOUS BIOMETRIC SYSTEMS
  17. 17. 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.
  18. 18.  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.
  19. 19. 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.
  20. 20. 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.
  21. 21. 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.
  22. 22.  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.
  23. 23. 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.
  24. 24.  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.
  25. 25. 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.
  26. 26. 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.
  27. 27. 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.
  28. 28. PHASE V FUTURE TRENDS
  29. 29.  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.
  30. 30.  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.
  31. 31. 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.
  32. 32. ANY QUESTIONS ???

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