Finger Print
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Transcript of Finger Print
Fingerprint Encoding and
Matching
FINGERPRINT IDENTIFICATION
Using Graph Method
Using minutiae and texture Method
Using Euclidian Distance
Fingerprint identification
using Graph matching
This technique use ridges, Not x-y coordinates and angle.
Identification of fingerprint
features
Ridge Orientation
Concept of Neighbors
In order to capture ridge-adjacency information, the
concept of neighbors is introduced. Neighbors come in
two varieties: end neighbors and side neighbors.
•End neighbors are those ridges that share a common
joining.
•Ridge Ri is said to "see" ridge Rj as a neighbor
if a perpendicular emanating from some point on Ri
intersects Rj without crossing any other ridge.
Example
Level Numbering
Graph Representation
Example of fingerprint
minutiae and their graph
representation
Repairing fingerprints
defects
Special minutiae and their
graph
Solid-state fingerprint
sensor
1. Challenge for traditional algorithms
2. Small contact area 0:6"0:6"
3. Less minutiae points
Optical Digital Biometrics sensor
1. Contact area 1” X 1”
2. 480 X 508 pixels
3. More minutiae points
Information Extracted
Suitable approach?
The minutiae based matching schemes will not perform well in such situations due to the lack of a sufficient number of minutiae points between the two impressions.
Suitable approach
Hybrid approach to fingerprint matching that combines a minutiae-based representation of the fingerprint with a Gabor-filter
(texture-based) representation for matching purposes.
Image alignment
Matching
Matching an input image with a stored template involves computing the sum of the squared differences between the two feature vectors after discarding missing values. This distance is normalized by the number of valid feature valuesused to compute the distance. The matching
score is combined with that obtained from the minutiae-based method, using the sum rule of combination. If the matching score is less than a predefined threshold, the input image is said to have successfully matched with the template.
CONCLUSIONS
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Algorithm Level Design
•Minutia Encoding
•Matching
•Return Match Score
Minutia Matcher:
Euclidian distance o Find Euclidian distance of first minutia by itself and all
of the other minutia's.
o Find the Euclidean distance of the database image as above.
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•Minutia Encoding
Given Parametero X and Y coordinates of minutia
o Orientation of the minutia
o Type of minutia ridge/bifurcation.
Parameter neededo X and Y coordinates of minutia
o Orientation of the minutia
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Minutia Encoding
X-axis Y-axis Theta type
150 260 3.86 1
112 235 2.56 1
124 256 2.50 0
160 459 1.45 0
For database image
oX and Y coordinates of minutia
oOrientation of the minutia
oType of minutia ridge/bifurcation
For database image
oX and Y coordinates of minutia
oOrientation of the minutia
oType of minutia ridge/bifurcation
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Minutia Encoding
X-axis Y-axis Theta type
260 260 5.86 1
431 245 7.56 1
114 156 1.50 0
120 359 1.45 0
Algorithm
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Database image Input image
Encoding of database
imageEncoding of input
image
Not matched
Matching
If (e1-e2)<10
&(θ1-θ2)<2
i=i+1
If(i>20)
Match
yes
no
yes no
e1=Euclidean dist of 1st image
e2=Euclidean dist of second image
i=counter
Fingerprint Encoding and
matching
Distance between neighboring minutiae• Delaunay triangulation
• This method can be accessed in MATLAB via the Delaunay function.
• The smallest value from the resulting list of distance values is then chosen, which gives us the distance from the minutiae to its nearest neighboring point.
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Fingerprint Verification
Thanks