Automatic Fingerprint Verification

1
Automatic Fingerprint Verification

• Principal Investigator
• Venu Govindaraju, Ph.D.
• Graduate Students
• T.Jea, Chaohang Wu, Sharat S.Chikkerur

2
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

3
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

• Research at CUBS Includes
• Fingerprint Image Enhancement
• Minutiae Feature Extraction
• Point pattern matching

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Fingerprint Image Enhancement

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

High contrast print
Typical dry print
Low contrast print
Typical Wet Print
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Traditional Approach

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

Local Orientation ?(x,y) Gradient Method
Enhancement Frequency/Spatial
Local Ridge Spacing F(x,y) Projection Based Method
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Fourier Analysis Approach

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

Energy Map E(x,y)
FFT Analysis
Orientation Map O(x,y)
FFT Enhancement
Ridge Spacing Map F(x,y)
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Fourier Analysis
Fingerprint ridges can be modeled as an oriented
wave
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Fourier Analysis Energy Map

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

Original Image
Energy Map
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Fourier Analysis Frequency Map

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

Original Image
Local Ridge Frequency Map
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Fourier Analysis-Orientation Map

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

Original Image
Local Ridge Orientation Map
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Fourier Domain Based Enhancement

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

Original Image
Enhanced Image
15
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

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

• 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.

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Pruning Detected Minutiae

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

• 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

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Secondary Features

• Pure localized feature
• Derived from minutiae representation
• Orientation invariant
• Denote as (r0, r1, d0, d1, ?)
• r0, r1 lengths of MN0 and MN1
• d0, d1 relative minutiae orientation w.r.t. M
• ? angle of N0MN1

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

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Dynamic Tolerance Areas

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

• 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.

Dynamic Windows
Dynamic tolerance
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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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Validation

• Preprocessing
• Enhancement
• Feature Extraction
• Matching

OD0.7865

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
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Preliminary Results
Threshold
FAR
FRR

• Min Total Error 0.00
• EER 0.0
• FRR at 0 FAR 0.0

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Thank You

• http//www.cubs.buffalo.edu