Identification Simplified - An Introduction to Biometrics

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This is the introductory presentation we did for Biometrics as our English project March 2009 :)

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  • I hear that biometric products, if used with a backup password, are now called a “below-one factor authentication”, since it makes the users less safe than a password-only single factor authentication. It is exactly like a house with two entrances is less safe against burglars than a house with one entrance. This means that biometric products must be used without a backup password if security is wanted. Can it be done? It should help a lot if you have a quick look at http://www.slideshare.net/HitoshiKokumai/blind-spot-in-our-mind-eyecatching-experience
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  • Nice presentations. Thanks
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  • Hi everyone and welcome to our presentation – “Identification Simplified”<number>
  • What we need today<number>
  • is a method to identify individuals which<number>
  • is secure and convenient.<number>
  • Lets look at what methods we use today to identify people…<number>
  • We have ID cards and Smart cards today<number>
  • We have even started using RFID cards which is a slightly more advanced type of card.<number>
  • We use passwords to protect our identity at work and internet<number>
  • We even use wristbands, you can find these in amusement parks mostly<number>
  • So, as you can see we have done a lot to solve the problem of identification… <number>
  • but yet we feel something is missing…<number>
  • You must be wondering what’s wrong with all these great methods of identification…<number>
  • With Card Type of identities, we got serious problems {click}The can be stolen {click}Can be duplicated {click}Have got limits {click}This ID card that I’m holding, Identifies me in the form of BG107CS***, which means it can address people from bg107cs000 to bg107cs999. Now that means we can address only 1000 people with it. You can clearly see that as the CS batch increases in size beyond 1000, this ID card needs to be replaced.<number>
  • Now that! Is how a modern professional’s wallet looks like today. Isn’t that loaded with cards!!<number>
  • Now passwords! Yes, even they’ve got problems<number>
  • Easily forgotten<number>
  • They need to be changed time to time. This ensures security and limits eavesdropping. many organizations have a policy to change everyone’s password after a fixed time.<number>
  • Now that comes at a cost. Here is a businessweek report saying that “Password is expensive”<number>
  • And a researchsers at forrester research have estimated this cost to be $10-$13 per employee.<number>
  • Not secure not convenient… we HAVE to find an answer to all these problems! and it won’t be some space-age technology… will it be in a chip embedded into us…No!!<number>
  • the answer is none of those… but the answer simply lies within us!<number>
  • In the form of biometrics…<number>
  • Now Biometrics is the… {definition }<number>
  • As I said, biometrics is the answer to the problems, you must be wondering how ?<number>
  • For that, we will now take you through various technologies that help us identify people under Biometircs.<number>
  • These are some of the techniques we would be discussing today. We would also talk about the Future and draw some conclusions based on our findings.<number>
  • I would now call upon Supraja to explain you how Iris Recognition is helpful in Biometrics.<number>
  • Identification Simplified - An Introduction to Biometrics

    1. 1. Identification Simplified
    2. 2. What we need ?
    3. 3. What we need ? a method to identify individuals which is -
    4. 4. What we need ? a method to identify individuals which is - Secure
    5. 5. What we need ? a method to identify individuals which is - Secure Convenient
    6. 6. How do we do that ?
    7. 7. How do we do that ? ID Cards & Smart Cards
    8. 8. How do we do that ? RFID Cards • RFID – Radio Frequency Identification
    9. 9. How do we do that ? Passwords
    10. 10. How do we do that ? Wrist Bands
    11. 11. How do we do that ? Wrist Bands too !! Wrist Bands Passwords ID Cards & Smart Cards RFID Cards
    12. 12. How do we do that ? Wrist Bands Passwords ID Cards & Smart Cards RFID Cards Wow! So many ways to identify people! what could be wrong?
    13. 13. What’s wrong with all that ?
    14. 14. What’s wrong with all that ? Can be Stolen! ID Cards Easily Duplicated Smart Cards Have got Limits RFID Cards Wrist Bands BG107CS*** Allocates from BG107CS000 to BG107CS999 Total 1000 students!
    15. 15. What’s wrong with all that ?
    16. 16. What’s wrong with all that ? Passwords
    17. 17. What’s wrong with all that ? Easily Forgotten Passwords
    18. 18. What’s wrong with all that ? Easily Forgotten Passwords Needs to be Changed
    19. 19. What’s wrong with all that ? Easily Forgotten Passwords Needs to be Changed
    20. 20. What’s wrong with all that ? Easily Forgotten Passwords Needs to be Changed The Password Is quot;Expensive“ It typically costs a company $10 to $13 to reset an employee password, according to Forrester Research
    21. 21. The answer to all this…
    22. 22. The answer to all this… Lies Within Us!
    23. 23. In the form of Biometrics
    24. 24. In the form of Biometrics Biometrics is the study of computerized methods to identify a person by their unique physical or behavioural characteristics
    25. 25. In the form of Biometrics
    26. 26. What’s in store for you?
    27. 27. What’s in store for you? Iris Recognition Voice Recognition Face Recognition Fingerprint Authentication Other Types
    28. 28. Iris Recognition S. Supraja
    29. 29. Iris Recognition The Basics
    30. 30. Iris Recognition The Basics • Colored portion of the eye • Unique •Single enrollment for a lifetime • Can be used with glasses or contacts
    31. 31. Iris Recognition The Process 1. Image Acquisition
    32. 32. Iris Recognition The Process 1. Image Acquisition 2. Iris Definition
    33. 33. Iris Recognition The Process 1. Image Acquisition 2. Iris Definition 3. Field Optimization
    34. 34. Iris Recognition The Process 1. Image Acquisition 2. Iris Definition 3. Field Optimization 4. Image Analysis
    35. 35. Iris Recognition Iris Code Comparison
    36. 36. Iris Recognition Iris Code Comparison
    37. 37. Iris Recognition Iris Code Comparison
    38. 38. Iris Recognition Iris Code Comparison Mismatch = (204/2048)*100 = 10%
    39. 39. Iris Recognition It’s Fool-Proof • 65.8% match • Un-modifiable • Pupil Dilation • No replacement for a live eye
    40. 40. Voice Recognition “Voice verification required.” “My voice is my password. Give me access.” “Voice Print verified. Identity confirmed. Access Granted.” Sahana V.
    41. 41. Voice Recognition
    42. 42. Voice Recognition
    43. 43. Voice Recognition
    44. 44. Voice Recognition
    45. 45. Voice Recognition
    46. 46. Voice Recognition
    47. 47. Voice Recognition Approach
    48. 48. Voice Recognition Approach Waveform Frequency Domain Time 1. Text dependent-hidden markov model 2. Text independent-vector quantization
    49. 49. Voice Recognition How is it done ?
    50. 50. Voice Recognition How is it done ? Two Waveforms of same word Amplitude Time •Waveform - the shape of a wave illustrated graphically • Amplitude - displacement of a periodic wave
    51. 51. Voice Recognition How is it done ? Two Waveforms of same word “Sample” “Sample” Amplitude Time Are Different
    52. 52. Voice Recognition How is it done ? But the Frequency-Time plots “Sample” “Sample” Frequency Time •Frequency - The number of occurrences of an event within a given interval
    53. 53. Voice Recognition How is it done ? But the Frequency-Time plots “Sample” “Sample” Frequency Time Look Similar
    54. 54. Voice Recognition Applications 1. Voice recognition for Authentication 1. Single pass phrase system 2. Text prompt system 3. Verification integrated in a dialogue system
    55. 55. Voice Recognition Applications 1. Voice recognition for Authentication 1. Single pass phrase system 2. Text prompt system 3. Verification integrated in a dialogue system 2. Voice recognition for Surveillance Forensic Voice Recognition
    56. 56. Face Recognition Pooja Sastry
    57. 57. Face Recognition Features • Facial Features are Unique
    58. 58. Face Recognition Features • Facial Features are Unique • Passive Biometrics
    59. 59. Face Recognition Features • Facial Features are Unique • Passive Biometrics • Already Widespread
    60. 60. Face Recognition Features • Facial Features are Unique • Passive Biometrics • Already Widespread • Inexpensive
    61. 61. Face Recognition Categories
    62. 62. Face Recognition Categories 1. Face Geometry
    63. 63. Face Recognition Categories 1. Face Geometry 2. Facial Thermograms
    64. 64. Face Recognition Categories 1. Face Geometry 2. Facial Thermograms 3. Eigen Face Method
    65. 65. Face Recognition Categories 1. Face Geometry 2. Facial Thermograms 3. Eigen Face Method 4. Template Based
    66. 66. Face Recognition Process
    67. 67. Face Recognition Process Detection
    68. 68. Face Recognition Process Alignment & Measurement
    69. 69. Face Recognition Process Representation & Matching
    70. 70. Face Recognition Process Identity Confirmed
    71. 71. Face Recognition Identification WHO AM I?
    72. 72. Face Recognition Verification AM I WHO I SAY I AM ?
    73. 73. Fingerprint Authentication S. Krithika
    74. 74. Fingerprint Authentication Features
    75. 75. Fingerprint Authentication Features •Ridges, Valleys
    76. 76. Fingerprint Authentication Features •Ridges, Valleys Ridges Valleys
    77. 77. Fingerprint Authentication Features •Ridges, Valleys •Dark lines - Ridges •Brighter lines - Valleys Ridges Valleys
    78. 78. Fingerprint Authentication Discriminating Information
    79. 79. Fingerprint Authentication Discriminating Information 1. Ridge Termination 2. Ridge Bifurcation
    80. 80. Fingerprint Authentication Authentication Procedure
    81. 81. Fingerprint Authentication Authentication Procedure Two essential procedures • Enrollment • Authentication
    82. 82. Fingerprint Authentication Authentication Procedure
    83. 83. Fingerprint Authentication Authentication Procedure Image Acquisition Classified roughly as • Optical • Non-Optical
    84. 84. Fingerprint Authentication Authentication Procedure
    85. 85. Fingerprint Authentication Authentication Procedure Feature Extraction • Direct • Feature or Minutia based
    86. 86. Fingerprint Authentication Authentication Procedure
    87. 87. Fingerprint Authentication Authentication Procedure Matching • 1 : 1 Matching • 1 : N Matching
    88. 88. Fingerprint Authentication Authentication Procedure
    89. 89. Fingerprint Authentication Authentication Procedure
    90. 90. Fingerprint Authentication Applications
    91. 91. Fingerprint Authentication Applications • Biometric Smart Gun
    92. 92. Fingerprint Authentication Applications • Biometric Smart Gun • Olympic Summer Games - Athens, Greece 2004
    93. 93. Fingerprint Authentication Drawback
    94. 94. Fingerprint Authentication Drawback • Skin Diseases prevent normal formation • Magali (Naegeli) syndrome • The HuangTien family in Taiwan
    95. 95. Fingerprint Authentication Drawback • Skin Diseases prevent normal formation • Magali (Naegeli) syndrome • The HuangTien family in Taiwan
    96. 96. Other Types Subhash Choudhary
    97. 97. Other Types Palm/Hand Geometry
    98. 98. Other Types Palm/Hand Geometry • Full-Hand Geometry
    99. 99. Other Types Palm/Hand Geometry • Full-Hand Geometry • Two Finger Scan
    100. 100. Other Types Palm/Hand Geometry • Full-Hand Geometry • Two Finger Scan Image Acquisition
    101. 101. Other Types Palm/Hand Geometry • Full-Hand Geometry • Two Finger Scan Image Acquisition Outline
    102. 102. Other Types Palm/Hand Geometry • Full-Hand Geometry • Two Finger Scan Image Acquisition Outline Geometry
    103. 103. Other Types DNA
    104. 104. Other Types DNA • Requires Actual Physical Sample
    105. 105. Other Types DNA • Requires Actual Physical Sample • Not fully automated
    106. 106. Other Types DNA • Requires Actual Physical Sample • Not fully automated
    107. 107. Other Types DNA • Requires Actual Physical Sample • Not fully automated • Expensive
    108. 108. Other Types DNA • Requires Actual Physical Sample • Not fully automated • Expensive • Privacy Issues
    109. 109. Other Types Gait
    110. 110. Other Types Gait • Study of Animal Locomotion
    111. 111. Other Types Gait • Study of Animal Locomotion • Helps Athletes
    112. 112. Other Types Gait • Study of Animal Locomotion • Helps Athletes • Standard Camera – any condition
    113. 113. Other Types Gait • Study of Animal Locomotion • Helps Athletes • Standard Camera – any condition • Identification at a Distance
    114. 114. Other Types Skin Reflection
    115. 115. Other Types Skin Reflection • Absorption Spectrum of Skin varies
    116. 116. Other Types Skin Reflection • Absorption Spectrum of Skin varies • LEDs send Light into Skin
    117. 117. Other Types Skin Reflection • Absorption Spectrum of Skin varies • LEDs send Light into Skin • Photodiodes read reflected light
    118. 118. Future of Biometrics Ratnala Srikanth
    119. 119. The Past & The Present
    120. 120. Future of Biometrics Security & Privacy Biometrics in Action Cost & Accuracy
    121. 121. Security & Privacy Biometric Systems must be Fool-Proof Security of Collected Biometric Data
    122. 122. Biometrics In Action
    123. 123. Biometrics In Action Immigration Control
    124. 124. Biometrics In Action Immigration Control Biometric Data is embedded in UK Passports
    125. 125. Biometrics In Action Transactional Authentication
    126. 126. Biometrics In Action Transactional Authentication A Biometric ATM in Malawi (South Africa)
    127. 127. Biometrics In Action Computer Security
    128. 128. Biometrics In Action Computer Security Finger Scan In Modern Laptops
    129. 129. Biometrics In Action Smart Doors
    130. 130. Biometrics In Action Smart Doors A New way for Physical Access Control
    131. 131. Biometrics In Action Time and Attendance
    132. 132. Biometrics In Action Time and Attendance A New Tamper-Proof way…
    133. 133. Cost & Accuracy More Accuracy = More Cost
    134. 134. Cost & Accuracy More Accuracy = More Cost
    135. 135. Conclusion
    136. 136. Conclusion End of Paper based Identity
    137. 137. Conclusion End of Identity Theft
    138. 138. Conclusion Reduces Expenditure on Security Saves Time Helps us Fight Terrorism Comfortable life + Improved Security
    139. 139. Conclusion We need to - • do Research in Biometrics • bring Awareness amongst people • Be ready for the change • Bring about a change • Become Smart
    140. 140. Further Reading • Paul Reid . Biometrics for Network Security . Prentice Hall PTR . December 30, 2003 • Nalini K Ratha . Advances in Biometrics . Springer . 2007 • Biometrics Consortium - •http://www.biometrics.org/resources.php • Biometrics Products – http://www.biometricsproducts.com/ • Introduction to Biometrics - http://biometrics.gov/ReferenceRoom/Introduction.aspx
    141. 141. Thank You! Abhishek Mishra S. Krithika Pooja Sastry Ratnala Srikanth Sahana V. S. Supraja Subhash Choudhary
    142. 142. Questions ?

    ×