EE 7740

EE 7740

Fingerprint Recognition

Bahadir K. Gunturk 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.)

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Biometrics

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Biometrics

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Biometrics

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Fingerprint Human fingerprints have been discovered on a large

number of archeological artifacts and historical items.

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Fingerprint 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 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.

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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.

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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.

Bifurcation

Termination

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Example 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 There are many “ambiguous” fingerprints, whose

exclusive membership cannot be reliably stated even by human experts.

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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.

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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.

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Minutiae Detection 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