Transcript EE 7730: Lecture 1 - Louisiana State University

EE 7740
Fingerprint Recognition
Biometrics
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Biometric recognition refers to the use of distinctive
characteristics (biometric identifiers) for automatically
recognition individuals.
These characteristics may be
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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.)
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Biometrics
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Biometrics
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Biometrics
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Fingerprint
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Human fingerprints have been discovered on a large
number of archeological artifacts and historical items.
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Fingerprint
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In 1684, an English plant morphologist published the
first scientific paper reporting his systematic study on
the ridge and pore structure in fingerprints.
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Fingerprint
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Fingerprint
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A fingerprint image may be classified as
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Offline:
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Inked impression of the fingertip on a paper is scanned
Live-scan:
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Optical sensor, capacitive sensors, ultrasound sensors, …
Critical parameter are:
Resolution, area, contrast, noise,
geometric accuracy.
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Fingerprint
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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.
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Fingerprint
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At the local level, there different local ridge characteristics.
The two most prominent ridge characteristics, called minutiae, are:
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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
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Example
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Matching is not easy due to: displacement, rotation,
partial overlap, nonlinear distortion, changing skin
condition, noise, feature extraction errors, etc.
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Example
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There are many “ambiguous” fingerprints, whose
exclusive membership cannot be reliably stated even
by human experts.
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Fingerprint Recognition Approaches
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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.
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Minutiae Detection
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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.
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Minutiae Detection
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Minutia detection may be followed by post-processing
to remove false minutiae structures.
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Fingerprint Matching
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Fingerprint Matching
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Fingerprint Matching
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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%
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Performance
• How to improve
• Fingerprint enhancement
• Estimating deformations
• Multiple matchers & combine results
• Multimodel biometrics
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