lec14 Finger Print Recognition-II

lec14 Finger Print Recognition-II - Fingerprint Matching...

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CSE190a Fal 06 Finger Print Recognition using Minutiae Biometrics CSE 190-a Lecture 10 © Jain, 2004 Fingerprint Matching Fingerprint Matching • Find the similarity between two fingerprints Fingerprints from the same finger Fingerprints from two different fingers © Jain, 2004 Fingerprint Sensors Fingerprint Sensors • Optical, capacitive, ultrasound, pressure, thermal, electric field © Jain, 2004 Fingerprint Classification Fingerprint Classification • Assign fingerprints into one of pre-specified types Plain Arch Tented Arch Right Loop Left Loop Accidental Pocket Whorl Plain Whorl Double Loop © Jain, 2004 Terminology Terminology Fingerprint – Impression of a finger Minutiae – Ridge bifurcations,endings and many other features (52 types listed, 7 are usually used by human experts and two by automated systems) Core – uppermost point on the innermost ridge Delta – separating point between pattern area and non-pattern area © Jain, 2004 Fingerprint Representation Fingerprint Representation • Local ridge characteristics ( minutiae ): ridge ending and ridge bifurcation • Singular points: Discontinuity in ridge orientation Core Delta Ridge Ending Ridge Bifurcation
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© Jain, 2004 Minutiae-based Representation Minutiae-based Representation © Jain, 2004 Steps in Minutiae Extraction Steps in Minutiae Extraction • Orientation field estimation • Fingerprint area location • Ridge extraction • Thinning • Minutia extraction © Jain, 2004 Minutiae Extraction Algorithm Minutiae Extraction Algorithm © Jain, 2004 Minutiae Type Detection • A ridge pixel is a ridge ending, if the number of ridge pixels in the 8-neighborhood is 1 • A ridge pixel is a ridge bifurcation, if the number of ridge pixels in the 8-neighborhood is greater than or equal to 3 • A ridge pixel is a intermediate ridge pixel, if the number of ridge pixels in the 8-neighborhood is 2 • [x, y, θ , associated ridge] are stored for each minutia © Jain, 2004 Minutiae Correspondences © Jain, 2004 Minutiae Matching Minutiae Matching • Point pattern matching problem •Le t ( ) ( ) { } P M P M P M P P P y x y x P θ , , ,..., , , 1 1 1 = be the set of M minutiae in the template image •L e t ( ) ( ) { } Q N Q N Q N Q Q Q y x y x Q , , ,..., , , 1 1 1 = be the set of N minutiae in the input image • Find the number of corresponding minutia pairs between P and Q and compare it against a threshold
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© Jain, 2004 Stages of Minutiae-based Verification • Extract Minutiae using corner detection • Characterize (label) Minutiae • Transformations between fingerprint images •RANSAC © Jain, 2004 Corner Detection © Jain, 2004 © Jain, 2004 Finding Corners Intuition: • Right at corner, gradient is ill-defined.
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This note was uploaded on 02/14/2008 for the course CSE 190A taught by Professor Kriegman during the Fall '06 term at UCSD.

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lec14 Finger Print Recognition-II - Fingerprint Matching...

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