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Fingerprint Classification by SOM
Wuzhili Vincent 99050056
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Introduction
Detailed Algorithms
Result Analysis
Demo
Fingerprint Classification by SOM
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IntroductionWhy goes to this topic: 1. Explore the Industrial Usage of SOM ”Fingerprint Classification Through Self Organizing
Feature Maps Modified to Treat Uncertainties” Proceedings of the IEEE, Vol 84, No 10, pp 1497-1512, October 1996
2. Easily start by extending from my Honors Project
“Fingerprint Matching”
Fingerprint Classification by SOM
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Introduction – Topics about Fingerprint
Fingerprint Classification by SOM
Fingerprint Sensor
Fingerprint Image Preprocessing
Fingerprint Matching
Fingerprint Image
Compression
Fingerprint Classification
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Fingerprint Manually Classifiedby Experts
Left Loop Right Loop
Whorl Arch Tented Arch
DeltaPore
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Introduction - Automated Classification
1. Might not according to the traditional 5-class scheme
2. Any uniformly distributed categories3. Consistently and correctly hash new
fingerprints into the categories
Fingerprint Classification by SOM
Class1
Class2
Class3
Class4
Class n
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How to Classify Fingerprints by SOM
Introduction - SOM
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4
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X1
X2
X3
An Input vector X = {x1,x2,x3}
w11w13
w12w14
2x2 SOM
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How to Classify Fingerprints by SOM
For a well-trained SOM:
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4
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X1
X2
X3
An Input vector X = {x1,x2,x3}
w11w13
w12w14
2x2 SOM
Winning Node
So the Input vector X is class 3 !
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How to Classify Fingerprints by SOM
The Feature Vector of a Fingerprint X:1. X has dimension 1 x 256: {x1,x2,….x256}2. It is the directional Map
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Extract the fingerprint region(right)
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Extract the Effective Region
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Locating Fingerprint Core
90% fingerprints centersare located
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Uncertainty Value: [0, 1]
1.Directions in the good-quality region has good certainty;
2.In the Left figure:Larger certainty ->longer amplitude
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Training Algorithm1: Original SOM
1. Contruct a MxM SOM, initialize all the weights2. Input a fingerprint vector: {x1,x2,….x256}3. Find the winning node dmin where: Dmin = min{||x-w||}4. Update the weight vectors:
W(new) =W(old) + Alpha*N*[x-w]Where N is the neighborhood function corresponding to the SOMnode topology
5. Repeat 2-4 till Update is not significant
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Training Algorithm2: Modified SOM
Note: Each fingerprint is associated with a certainty vector C
1. Contruct a MxM SOM, initialize all weights2. Input a fingerprint vector:
X{x1,x2,….x256} = C*X + (1-C)*Xavg;3. Find the winning node dmin where: Dmin = min{||x-w||}4. Update the weight vectors:
W(new) =W(old) + Alpha*N*[x-w] * CWhere N is the neighborhood function corresponding to the SOMnode topology
5. Repeat 2-4 till Update is not significant
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Experiment
Fa Fb Fc….. FA FB FC
1. Fa and FA are from the same finger Fb and FB … Fc and FC …2. Each fingerprint in DataA belongs to a class Class(Fa) = k , k within [1 ~ mxm]
Training Set (DataA) Testing Set (DataB)
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Experiment
Fa Fb Fc….. FA FB FC
If all fingerprints are uniformly classified,Less accumulated worst search price-> Less DataA fingerprints are searched when indexing DataB
Training Set (DataA) Testing Set (DataB)
Class(FA) = ClassX
ClassXThe worst search price to find Fa is Size(ClassX)
Fa
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SEARCH% RECOGNIO ON%
3x3 4x4 5x5 8x8 10x10M 10 12.1 18.8 28.8 91.8 100S 20 26.0 38.7 54.0 100 O 30 40.1 61.3 80.5 M 40 55.9 86.6 100 50 71.9 100 60 88.5 70 100 80 90 100
10 19.6 16.4 26.4 40.0 62.7 S 20 39.6 38.1 53.1 100 100O 30 57.3 60.9 82.5 M 40 72.9 84.8 100 50 86.3 100 60 97.5 70 100 80 90 100
ResultsSearch% column : percentage searched in DataARecognition% Column: percentage found for DataB
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Advanced work can be done:
1.Increase the layer of SOM to solve the crowded class with many fingerprints2. Principle Component Analysis to reduce the feature vectorfrom 256 to small dimensions. [40 dimensions are feasible]
