Transcript
Fingerprint Identification Based on Minutiae Point Using Probabilistic Neural NetworkEnny Indasyah, Septian Enggar S., Shi Jihn Horng, Ketut Edi P., Mauridhi Hery Purnomo1. Electrical Engineering Department - Institut Teknologi
Sepuluh Nopember2. Department of Computer Science and Information
Engineering - National Taiwan University of Science and Technology (NTUST)
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outline
Introduction Contribution Algorithm Result Conclusion and Next Research
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Introduction
Fingerprint identification problem Fingertip surface
conditionSpecial skin condition affects fingerprints identification perform
Feature points are hard to be extracted by image processing
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Contribution
To produce better fingerprint identification result on special skin condition
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AlgorithmImage acquisition
Image binarization
Object Thinning
Minutiae feature extraction
Identification based PNN
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Image acquisition
Fingerprint image
Obtained from fingerprint sensor
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Image binarizationRule:Pixel values is set to 255 if the pixel is less than the threshold level
Threshold level = 160
Before binarization
after binarization
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Thinningto eliminate or reduce the width of ridges till the ridges are one pixel
to simplify the subsequent structural analysis of the image for the extraction of the fingerprint minutiae
Before thinning after thinning
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Minutiae feature extraction
By using Crossing Number (CN) concept
1. Whether a pixel, belongs to a ridge or not.
2. if the central pixel is 1 and has exactly 3 one-value neighbors, then the central pixel is a ridge bifurcation.
3. If the central pixel is 1 and has only 1 one-value neighbor, then the central pixel is a ridge ending (termination). 0 1 0
0 1 0
1 0 1
0 1 0
0 1 0
0 0 1
bifurcation termination
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Minutiae feature extraction
Removal spurious minutiae1. if the distance between a
termination and a bifurcation is smaller than D.
2. if the distance between two bifurcations is smaller than D.
3. if the distance between two terminations is smaller than D.
What is D?D is average distance between two neighboring ridges
Parameters of minutiae point a) bifurcation and b) ridge ending (termination)
Minutiae Extraction before removal process
Minutiae Extraction after Removal process
Green circle is bifurcation and red circle is termination
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Identification based PNNInput layer:Minutiae feature
hidden layer:Classifying the minutiae
output layer:Result of identification
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Result-Minutiae extraction
X Y Angle2 79 2.0095 2 113.002 128 2.00
145 2 177.002 192 4.0062 5 49.007 35 8.00
Parameter of termination
Parameter of bifurcationX Y Angle 1 Angle
2Angle
38 106 20.00 94.00 19.00
141 27 135.00 42.00 123.0050 117 52.00 185.00 57.0088 56 180.00 62.00 191.0063 45 64.00 84.00 66.00
186 68 32.00 75.00 29.0076 49 80.00 120.00 82.00
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Result-PNN’s performance
0 10 20 30 40 50 60 70 80 90 1001
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10The Result of Data Testing for PNN Classification
targetresult
100 testing data and 10 class fingerprint
the vertical axis is classification results from 10 different class. Horizontal axis is the total of testing data from 10 different class.
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Conclusion & Next Research
Robust fingerprint preprocessing methods
Better results of identification
Next research
The experimental results show very good performance both on training and testing from database
Conclusion
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Thank You
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