fingerprint recognition through circular sampling l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Fingerprint Recognition Through Circular Sampling PowerPoint Presentation
Download Presentation
Fingerprint Recognition Through Circular Sampling

Loading in 2 Seconds...

play fullscreen
1 / 26

Fingerprint Recognition Through Circular Sampling - PowerPoint PPT Presentation


  • 128 Views
  • Uploaded on

Fingerprint Recognition Through Circular Sampling. David Chang* and Joseph Hornak Rochester Institute of Technology Rochester, NY 14623-5604. Introduction. Useful for personal identification. Valuable to criminal investigators and forensic scientists. Overview. Introduction Background

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

Fingerprint Recognition Through Circular Sampling


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
fingerprint recognition through circular sampling

Fingerprint Recognition Through Circular Sampling

David Chang* and Joseph Hornak

Rochester Institute of Technology

Rochester, NY 14623-5604

introduction
Introduction
  • Useful for personal identification.
  • Valuable to criminal investigators and forensic scientists.
overview
Overview
  • Introduction
  • Background
  • Theory
  • Methods
  • Results
  • Conclusion
  • Future Work
background
Background
  • Fingerprint Uniqueness
  • Obstacles
  • AFIS Method
fingerprint uniqueness
Fingerprint Uniqueness
  • Major: Central pattern
    • arch, loop, whorl
fingerprint uniqueness7
Fingerprint Uniqueness
  • Minor: Minutiae
    • ridge termination, bifurcation
obstacles
Obstacles
  • Rotation
  • Displacement
  • Missing area
  • Image defects
afis method
AFIS Method
  • Image enhancement
  • Feature extraction
  • Feature mapping
  • Classification via flow maps
  • Matching
    • # of minutiae
    • Euclidean distances
theory sampling process11
Theory: Sampling Process

Fingerprint Image

Concentric Circle Sample

match metric area ratio

A = 8

A = 16

MArea Ratio = 0.5

Match Metric: Area Ratio
  • Average of ratio between the areas of corresponding circles in the two samples being matched.
match metric correlation fraction

A = 8

A = 16

MAX = 6

MCorrelation Fraction = 0.75

Match Metric: Correlation Fraction
  • Average of the max value in the correlated signal divided by smaller area of corresponding circles in the two samples being matched.
match metric angular density

Low Match Probability

High Match Probability

Match Metric: Angular Density
  • Determine mean square error (MSE) among angles corresponding to highest magnitude in the correlation signal.

MAngular Density = 1 - 2(MSE)/

methods
Methods
  • Source Images
    • 48 Synthetic Fingerprint Images
      • 512 x 512 pixels at 1-bit/pixel
  • Match Matrices
  • 48 x 48 matrix where each column sample is matched against each row source sample.
    • Done for the 3 metrics.
    • Observe effects
      • Missing Areas
      • Rotation
  • Examine displacement effects
results unchanged variables

Area Ratio

Correlation Fraction

Angular Density

Results: Unchanged Variables
results arbitrary area removed

Area Ratio

Correlation Fraction

Angular Density

Results: Arbitrary Area Removed
results rotation effects

Area Ratio

Correlation Fraction

Angular Density

Results: Rotation Effects

Column images rotated 45

results displacement effects

MArea Ratio

MCorrelation Fraction

MAngular Density

Arch

16

0

-16

16

0

-16

16

0

-16

-16 0 16

-16 0 16

-16 0 16

Loop

16

0

-16

16

0

-16

16

0

-16

-16 0 16

-16 0 16

-16 0 16

Whorl

16

0

-16

16

0

-16

16

0

-16

-16 0 16

-16 0 16

-16 0 16

Results: Displacement Effects
conclusion
Conclusion
  • Of the three metrics, the angular density metric proves to be most effective.
  • Displacement effects show that a consistent selection of the circles center is necessary.
future work
Future Work
  • Image enhancement
    • Test on actual fingerprints
  • Observe effects of less circles
  • Test on larger database
  • Code optimization
fingerprint chicks
Fingerprint Chicks
  • Materials:
    • Yellow
    • Tempera paint
    • Wash tubs
    • Large construction paper
    • Glue
  • Directions:
    • 1.Each student chooses a construction paper for background.
    • 2.Have students come up one at a time, to gently dip their hands and fingers in the yellow tempera paint.
    • 3.Each will place their hands and fingers on their paper, making a fingerprint.
    • 4.Kids decorate their fingerprints to look like a new Spring chick.