Fingerprint Image Enhancement - PowerPoint PPT Presentation

1 / 10
About This Presentation
Title:

Fingerprint Image Enhancement

Description:

Fingerprint Image Enhancement Joshua Xavier Munoz-Ramos Motivation Method for fingerprint image enhancement Ridge structure in fingerprint images are not always well ... – PowerPoint PPT presentation

Number of Views:516
Avg rating:3.0/5.0
Slides: 11
Provided by: JoshuaXav
Category:

less

Transcript and Presenter's Notes

Title: Fingerprint Image Enhancement


1
Fingerprint Image Enhancement
  • Joshua Xavier Munoz-Ramos

2
Motivation
  • Method for fingerprint image enhancement
  • Ridge structure in fingerprint images are not
    always well defined therefore, enhancement
    algorithm, is necessary
  • A critical step in automatic fingerprint matching
    is extracting minutiae from the input fingerprint
    images. However, the performance of a minutiae
    extraction relies on the quality of the images.

3
Background
  • Two Important ridge characteristics
  • Ridge ending
  • Ridge bifurcation

4
Approach
  • Original Grayscale fingerprint image
  • Local histogram equalization
  • Wiener Filtering
  • Binarization and thinning
  • Morphological and further filtering (Anisotropic
    Filter)
  • Enhanced binary image
  • http//fvs.sourceforge.net/C27_icpr2000.pdf
  • Fingerprint Image Enhancement
  • Algorithm and Performance Evaluation
  • Lin Hong, Student Member, IEEE, Yifei Wan, and
    Anil Jain, Fellow, IEEE

5
(No Transcript)
6
Weiner filter
  • W(n1,n2)u (v2-n2)/(v2) I(n1,n2)-u
  • 3x3 matrix
  • Binary Thresholding (if I(n1,n2) gt local mean set
    to 1 other wise set to 0)

7
Weiner filter / binary
8
Morphing/ anisotropic filter
  • Connecting the ridges through orientation fields

9
Anisotropic filter
Instead of using local gradients as a means of
controlling the anisotropism of filters, it uses
both a local intensity orientation and an
anisotropic measure to control the shape of the
filter. K(x0,x) exp-((x-x0) n ) 2/sig(x0)2
((x-x0) n(ortho))2/sig(x0)2 h(x0,x)
-2 10k(x0,x)
10
Results
  • 71.1 percent FAR (using verification system )
    but only tested two fingerprints with 10
    different pics 7/10 were identified
  • False ridges endings and bifurcations
  • Need to test more fingerprints
  • A good image has around 40 to 50 correct ridge
    endings and bifurcations
  • (different method is to apply a garbor filter )
  • Fingerprint acceptance rate
  • (enhancement did not work as well as expected)
  • Picture was clearer to see after enhancement, and
    the filters did smooth out noise
  • However many false ridges and bifurcations
  • Many parts where the picture was not clear my
    enhancement did not work.
  • Future work. Fix the orientation field and the
    anisotropic filter.. Many details were lost.
  • (citation) http//fvs.sourceforge.net/C27_icpr2000
    .pdf
Write a Comment
User Comments (0)
About PowerShow.com