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Spattern matching using biometric techniques

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  • 1. PATTERN MATCHING USING BIOMETRIC TECHNIQUES For Documentation and Downloads Visit
  • 2. INTRODUCTION
    • Bio(Life), Metrics(Measure)
    • Biometrics can be defined as the science and technology of
    • measuring and statistically analyzing biological data.
    • Biometrics allow us to authenticate ourselves with things
    • that we carry with us wherever we may go, such as our
    • hands, eyes, voices, faces, fingerprints etc.
  • 3. HISTORY
    • British scientist Sir Francis Galton proposed the use of
    • fingerprints IN 19 TH Century.
    • One of the most well-known biometrics characteristics is
    • fingerprint.
    • Identification systems available from 1990’s.
  • 4. BIOMETRIC SYSTEM CHARACTERISTIC
    • Universal
    • Unique
    • Permanent
    • Collectable
  • 5. Basic architecture of a biometric system
  • 6. DIFFERENT METHODOLOGIES
    • Finger printing recognition
    • Hand geometry
    • Iris based identification
    • Retinal pattern recognition
    • Facial recognition
    • Signature recognition
    • Speaker verification
  • 7. FINGER PRINT IDENTIFICATION
    • Finger print technique is the Oldest of all the Biometric Techniques.
    • It is a pattern of Ridges and Furrows on surface
    • of Finger-tip.
    • Finger prints are unique and remains
    • Unchanged.
  • 8. HAND GEOMETRY Examines : hand shape, lengths and widths of the fingers, and the overall size of the hand.
    • This technique is simple,
    • relatively easy to use, and inexpensive.
  • 9. IRIS-BASED IDENTIFICATION
    • Each Iris is Unique.
    • It is scanned by a low intensity light source.
  • 10. RETINAL PATTERN RECOGNITION
    • It is the innermost layer of the eye.
    • The pattern formed by veins beneath the
    • surface of the retina is unique.
    • A low-intensity beam of visible or infrared
    • light into a person’s eye
  • 11. FACIAL RECOGNITION
    • Facial scanning involves scanning
    • of the entire face and checking of
    • critical points.
    • Generally used in association with other
    • biometric techniques.
    • Applied for both still photographs &
    • Video tape or movies.
  • 12. SIGNATURE RECOGNITION
    • Two approaches,
    1.Static signature verification. (determines only geometric features) 2.Dynamic signature verification. ( determines both geometric features and dynamic features)
  • 13. SPEAKER VERIFICATION
    • Depends on the frequency, stress and
    • accent of speech.
    • The main physiological aspect of the human speech production system is the
    • vocal tract shape.
  • 14. FINGER PRINT ANALYSIS
    • Two categories:
    • 1.Minutiae-based
    • 2.Correlation based
    Fig. Finger print matching
  • 15. FINGERPRINT PATTERN TYPES Loop Whorl . Arch
  • 16. FACTORS CONSIDERED IN SCANNING
    • Amount of pressure applied to the Fingertip .
    • The presence of any cuts or other deformities on the fingertip.
    • The dryness of the skin, edges of rides.
    • Ridge bifurcations, or branches—collectively known as minutiae
  • 17. MULTIBIOMETRICS
    • Integrating Faces and Fingerprints for Personal Identification.
    • A Multimodal Biometric System Using Fingerprint, Face, and Speech.
  • 18. APPLICATIONS
    • Prison visitor systems
    Driver’s licenses Canteen administration in a campus Benefit payment systems Border Control Voting systems Future Applications : ATM machine use Workstation and network access Travel and tourism Internet transactions Telephone transactions
  • 19. DRAWBACKS OF BIOMETRIC METHODS
    • RETINAL SCAN
    • IRIS RECOGNITION
    • FINGER IMAGING
    • HAND GEOMETRY
    • FACIAL RECOGNITION
    • VOICE RECOGNITION
    • SIGNATURE RECOGNITION
  • 20. CONCLUSION
    • The coming years are going to witness is giant expansion in hardware software changing conventions of speed on a much broader spectrum.
    Biometric systems are going to play a major role in coming future mostly in Security field.
  • 21. REFERENCES
    • [Bigun, 1997] Bigun, E.S., J. Bigun, Duc, B.: “Expert conciliation for multi modal person authentication systems by Bayesian statistics,” In Proc. 1st Int. Conf. On Audio Video-Based Personal Authentication
    • [Brunelli et al, 1995] R. Brunelli, and D. Falavigna, "Personal identification using multiple cues," IEEE Trans. on Pattern Analysis and Machine Intelligence.
    • [Dieckmann]Dieckmann, U.Plankensteiner, P., and Wagner, T.: “SESAM: A biometric person identification system using sensor fusion,” In Pattern Recognition Letters, Vol. 18, No. 9
  • 22. THAN"Q" For Documentation and Downloads Visit For Documentation and Downloads Visit
  • 23. QUERIES