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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 :)

  • 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
    Bharath
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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 ?

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