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Biometric security Presentation

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Biometric security Presentation

  1. 1. BIOMETRIC SECURITY SYSTEMS
  2. 2. A FUTURE WITHOUT PASSWORDS End of “PASSWORD OVERLOAD” WORKPLACE  DESKTOP COMPUTER  CORPORATE COMPUTER NETWORK  INTERNET & E-MAIL
  3. 3. CLASSIFICATION  PHYSIOLOGICAL OR  BEHAVIORAL  IDENTIFICATION OR  VERIFICATION
  4. 4. BIOMETRIC PROCESS ENROLLMENT Present Biometric Capture NO MATCH Process COMPARE VERIFICATION Present Biometric Capture STORE Process MATCH
  5. 5. TECHNOLOGIES BIOMETRICS IRIS SIGNATURE SECURITY HAND FINGER FACE VOICE
  6. 6. IRIS RECOGNITION  Iris patterns are extremely complex.  Patterns are individual  Patterns are formed by six months after birth, stable after a year. They remain the same for life.  Imitation is almost impossible.  Patterns are easy to capture and encode
  7. 7. RETINA SCANNERS (continued) •Main retina features •Actual photo of retina
  8. 8. IRIS SCANNERS • High resolution cameras capture image from up to 3 feet away (usually 10 to 12 inches) • Converts picture of the distinctive fibers, furrows, flecks, crypts, rifts, pits and coronas of the iris into a bar-code like identifier • Template around 256 Bytes in size • Human iris is distinct with 250 differentiating features • The recognition of irises by their IrisCodes is based upon the failure of a test of statistical independence. – Any given IrisCode is statistically guaranteed to pass a test of independence against any IrisCode computed from a different eye; but it will uniquely fail this same test against the eye from which it was computed.
  9. 9. FINGER RECOGNITION Print showing various types of Minutiae
  10. 10. HOW IT WORKS SUBMISSION FEATURE EXTRACTION IMAGE ENHANCEMENT MATCHING
  11. 11. • FINGER PRINT (continued ) Fingerprint matching techniques can be placed into two categories: minutiaebased and correlation based. – Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. However, there are some difficulties when using this approach. • It is difficult to extract the minutiae points accurately when the fingerprint is of low quality. • Also this method does not take into account the global pattern of ridges and furrows. – The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. However, it has some of its own shortcomings. • Correlation-based techniques (i.e. pattern matching) require the precise location of a registration point and are affected by image translation and rotation. • Larger templates (often 2 – 3 times larger than minutiae-based)
  12. 12. FACE RECOGNITION •Typical Eigenfaces •Utilizes two dimensional, •global grayscale images •representing distinctive •characteristics of •a facial image •Variations of eigenface are •frequently used as the basis of other face recognition methods.
  13. 13. FACIAL (continued) • • Eigenface: "one's own face," a technology patented at MIT that uses 2D global grayscale images representing distinctive characteristics of a facial image. Most faces can be reconstructed by combining features of 100-125 eigenfaces. During enrollment, the user's eigenface is mapped to a series of numbers (coefficients). Upon a 1:1 match, a "live" template is matched against the enrolled template to obtain a coefficient variation. This variation either accepts or rejects the user. Local Feature Analysis (LFA): also a 2D technology, though more capable of accommodating changes in appearance or facial aspect (e.g., smiling, frowning). LFA uses dozens of features from different regions of the face; incorporates the location of these features. Relative distances and angles of the "building blocks" of the face are measured. LFA can accommodate 25degree angles in the horizontal plane and 15 degrees in the vertical plane. LFA is a derivative of the eigenface method and was developed by Visionics, Corp.
  14. 14. FACE RECOGNITION
  15. 15. FACIAL (continued) • Varying light (i.e. outdoors) can affect accuracy • Some systems can compensate for minor changes such as puffiness and water retention • Smiling, frowning, etc can affect accuracy • Some systems can be confused by glasses, beards, etc • Human faces vary dramatically over long term (aging) and short term (facial hair growth, different hair styles, plastic surgery) • Expected high rate of acceptance as people are already used to being photographed or monitored • Best method for identification systems (e.g. airports)
  16. 16. VOICE RECOGNITION • The software remembers the way you say each word. • Voice recognition possible even though everyone speaks with varying accents and inflection. • Telephony : the primary growth area
  17. 17. VOICE VERIFICATION •A complete signal has an overall pattern, as well as a much finer structure, called the frame. This frame is the essence of voice verification technology. •It is these well-formed, regular patterns that are unique to every individual. These patterns are created from the size and shape of the physical structure of a person's vocal tract. Since no two vocal tracts are exactly the same, no two signal patterns can be the same.
  18. 18. VOICE VERIFICATION •These unique features consist of cadence, pitch, tone, harmonics, and shape of vocal tract. •The image at right shows how characteristics of voice actually involve much more of the body than just the mouth.
  19. 19. HAND GEOMETRY • 32,000-pixel CCD digital camera . • The hand-scan device can process the 3-D images in less than 5 seconds & the verification usually takes less than 1 second. • U.S INPASS PROGRAM
  20. 20. HAND/FINGER GEOMETRY (continued)
  21. 21. HAND/FINGER GEOMETRY READERS • The first modern biometric device was a hand geometry reader that measured finger length • These devices use a 3D or stereo camera to map images of the hands and/or fingers to measure size, shape and translucency • Actual sensor devices are quite large in size • Templates are typically small (approx 10 Bytes) • High acceptance rate among users
  22. 22. SIGNATURE RECOGNITION How the signature was made. i.e. changes in speed, pressure and timing that occur during the act of signing An expert forger may be able to duplicate what a signature looks like, but it is virtually impossible to duplicate the timing changes in X, Y and Z (pressure) 
  23. 23. SIGNATURE ANALYSIS (continued) •Built-in sensors register the dynamics of the act of writing. These dynamics include the 3D-forces that are applied, the speed of writing, and the angles in various directions. •This signing pattern is unique for each individual, and thus allows for strong authentication. It also protects against fraud since it is practically impossible to duplicate "how" someone signs.
  24. 24. • A multimodal biometric system uses the integration of biometric systems in order to meet stringent performance requirements. •Much more vital to fraudulent technologies
  25. 25. TOKENS, SMART CARDS & BIOMETRIC AUTHENTICATION SCHEMES  INTEGRATION IS ESSENTIAL
  26. 26. CONCLUSION Once the exclusive preserve of sci-fi books and movies, biometrics now has to be considered as one of the many challenges of modern day management. 

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