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EE 7740

EE 7740 . Fingerprint Recognition. Biometrics. Biometric recognition refers to the use of distinctive characteristics ( biometric identifiers ) for automatically recognition individuals. These characteristics may be Physiological (e.g., fingerprints, face, retina, iris)

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EE 7740

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  1. EE 7740 Fingerprint Recognition

  2. Biometrics • Biometric recognition refers to the use of distinctive characteristics (biometric identifiers) for automatically recognition individuals. • These characteristics may be • Physiological (e.g., fingerprints, face, retina, iris) • Behavioral (e.g., gait, signature, keystroke) • Biometric identifiers are actually a combination of physiological and behavioral characteristics, and they should not be exclusively identified into either class. (For example, speech is determined partly by the physiology and partly by the way a person speaks.)

  3. Biometrics

  4. Biometrics

  5. Biometrics

  6. Fingerprint • Human fingerprints have been discovered on a large number of archeological artifacts and historical items.

  7. Fingerprint • In 1684, an English plant morphologist published the first scientific paper reporting his systematic study on the ridge and pore structure in fingerprints.

  8. Fingerprint

  9. Fingerprint • A fingerprint image may be classified as • Offline: • Inked impression of the fingertip on a paper is scanned • Live-scan: • Optical sensor, capacitive sensors, ultrasound sensors, … Critical parameter are: Resolution, area, contrast, noise, geometric accuracy.

  10. Fingerprint • The fingerprint pattern exhibits different types of features. • At the global level, the ridge line flow has one the following patterns. Singular points are sort of control points around which a ridge line is “wrapped”. There are two types of singular points: loop and delta. However, these singular points are not sufficient for accurate matching.

  11. Fingerprint • At the local level, there different local ridge characteristics. • The two most prominent ridge characteristics, called minutiae, are: • Ridge termination • Ridge bifurcation • At the very-fine level, intra-ridge details (sweat pores) can be detected. They are very distinctive; however, very high-resolution images are required. Termination Bifurcation

  12. Example • Matching is not easy due to: displacement, rotation, partial overlap, nonlinear distortion, changing skin condition, noise, feature extraction errors, etc.

  13. Example • There are many “ambiguous” fingerprints, whose exclusive membership cannot be reliably stated even by human experts.

  14. Fingerprint Recognition Approaches • Correlation-based matching: Intensity based correlation between the fingerprint images are computed. • Minutiae-based matching: Minutiae are extracted from two fingerprints and stored as sets of points in the 2D plane. Matching is done based on minutiae pairings. • Ridge feature-based matching: Local orientation and frequency of ridges, ridge shape, texture, etc are used for matching.

  15. Minutiae Detection • Binarize the image (using global thresholding, local thresholding, etc.) • Apply thinning (by, for example, using morphological operations) to get the skeleton image. • Analyze the neighborhood of each pixel in the skeleton image.

  16. Minutiae Detection • Minutia detection may be followed by post-processing to remove false minutiae structures.

  17. Fingerprint Matching

  18. Fingerprint Matching

  19. Fingerprint Matching

  20. Performance • Comparison • Fingerprints [FVC 2002] • False reject rate: 0.2% • False accept rate: 0.2% • Face [FRVT 2002] • False reject rate: 10% • False accept rate: 1% • Voice [NIST 2000] • False reject rate: 10-20% • False accept rate: 2-5%

  21. Performance • How to improve • Fingerprint enhancement • Estimating deformations • Multiple matchers & combine results • Multimodel biometrics

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