Automatic Fingerprint Verification Principal Investigator Venu
Govindaraju, Ph.D. Graduate Students T.Jea, Chaohang Wu, Sharat
S.Chikkerur
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Conventional Security Measures Token Based Smart cards Swipe
cards Knowledge Based Username/password PIN Disadvantages of
Conventional Measures Tokens can be lost or misused Passwords can
be forgotten Multiple tokens and passwords difficult to manage
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Biometrics Definition Biometrics is the science of verifying
and establishing the identity of an individual through
physiological features or behavioral traits. Examples Physical
Biometrics Fingerprint Hand Geometry Iris patterns Behavioral
Biometrics Handwriting Signature Speech Gait
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Fingerprints as biometrics Established Science Forensic
institutions have used fingerprints to establish individual
identity for over a century. High Universality Every person
possesses the biometric High Distinctiveness Even identical twins
have different fingerprints though they have the same DNA. High
Permanence Fingerprints are formed in the foetal stage and remain
structurally unchanged through out life. High Acceptability
Fingerprint acquisition is non intrusive. Requires no
training.
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Introduction to Fingerprints Fingerprints can be classified
based on the ridge flow pattern Fingerprints can be distinguished
based on the ridge characteristics
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Fingerprint Verification System Good quality Image Good quality
Fingerprint Image Authentication Fingeprint Image Fingerprint Image
Enhancement Minutiae Feature Extraction Matching methods Database
Minutiae features Image Preprocessing Research at CUBS Includes
Fingerprint Image Enhancement Minutiae Feature Extraction Point
pattern matching
Preprocessing Enhancement Feature Extraction Matching Original
ImageEnhanced Image Fourier Domain Based Enhancement
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Enhancement Results
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Feature Extraction Methods Preprocessing Enhancement Feature
Extraction Matching Thinning-based Method Thinning produces
artifacts Shifting of Minutiae coordinates Direct Gray-Scale
Extraction Method Difficult to determine location and orientation
Binarized Image is noisy.
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Chaincoded Ridge Following Method Preprocessing Enhancement
Feature Extraction Matching
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Minutiae Detection Several points in each turn are detected as
potential minutiae candidate One of each group is selected as
detected minutiae. Minutiae Orientation is detected by considering
the angle subtended by two extreme points on the ridge at the
middle point. Preprocessing Enhancement Feature Extraction
Matching
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Pruning Detected Minutiae Ending minutiae in the boundary of
fingerprint images need to be removed with help of FFT Energy Map
Closest minutiae with similar orientation need to be removed
Preprocessing Enhancement Feature Extraction Matching
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Pure localized feature Derived from minutiae representation
Orientation invariant Denote as (r 0, r 1, 0, 1, ) r 0, r 1 :
lengths of MN 0 and MN 1 0, 1 : relative minutiae orientation
w.r.t. M : angle of N 0 MN 1 Secondary Features Preprocessing
Enhancement Feature Extraction Matching
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Dynamic Tolerance Areas Tolerance Area is dynamically decided
w.r.t. the length of the leg. Longer leg: Tolerates more distortion
in length than the angle. Shorter leg: tolerates less distortion in
length than the angle. A B O Preprocessing Enhancement Feature
Extraction Matching Dynamic tolerance Dynamic Windows
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Feature Matching Preprocessing Enhancement Feature Extraction
Matching 1.For each triangle, generate a list of candidate matching
triangles 2.To recover the rotation between the prints. Find the
most probable orientation difference 3.Apply the results of the
pruning and match the rest of the points based on the reference
points established.
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OD=0.7865 Validation Preprocessing Enhancement Feature
Extraction Matching 1.For each triangle, generate a list of
candidate matching triangles 2.To recover the rotation between the
prints. Find the most probable orientation difference 3.Apply the
results of the pruning and match the rest of the points based on
the reference points established.
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Minutia Matching Preprocessing Enhancement Feature Extraction
Matching 1.For each triangle, generate a list of candidate matching
triangles 2.To recover the rotation between the prints. Find the
most probable orientation difference 3.Apply the results of the
pruning and match the rest of the points based on the reference
points established
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Data Sets Fig(a) Sensors and technology used in acquisition
Fig(b) Paired fingerprintsFig(c) Database sets
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Preliminary Results Min Total Error = 0.00% EER = 0.0% FRR at 0
FAR = 0.0% FAR FRR Threshold