image pre-processing for classification (biometric identification) by a neural network anthony...
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IMAGE PRE-PROCESSING FOR CLASSIFICATION (BIOMETRIC IDENTIFICATION)
BY A NEURAL NETWORK
Anthony Vannelli, Steve Wagner, and Ken McGarveyHorizon Imaging, LLC
email: info@horizonimaging.com
Horizon Imaging, LLCInnovative Solutions in Image Processing
Raw 512x480 Image
Neural Preprocessor
Neural Network Classifier
Reduced data set
Classification Output
Neural Network Preprocessor and Classifier
• Wavelets
• PCA
• Image “Zones”
• Combining Networks
• Feed-forward Network
• Back-propagation Training
• Single Hidden Layer
Horizon Imaging, LLCInnovative Solutions in Image Processing
• 512x480 raw image or 245,760 inputs to network
• Large neural network
• Poor classification performance
• Slow convergence
Curse of dimensionality
Horizon Imaging, LLCInnovative Solutions in Image Processing
Biometric Identification
Region of Interest
320x160 = 51,200 pixels
Horizon Imaging, LLCInnovative Solutions in Image Processing
Preprocessing Techniques
• Non-parametric
• “Holistic”
• Data-driven
• No Hand Geometry
• No Fidiucial Points
Horizon Imaging, LLCInnovative Solutions in Image Processing
Preprocessing Techniques
• Principal components
• Large eigen-values help to classify
• Reduces dimensionality
• Image Processing Zones
• Divide and conquer
• 2x2 zones (160x80 pixels)
• 4x4 zones (80x40 pixels)
• Ensemble of neural networks
Horizon Imaging, LLCInnovative Solutions in Image Processing
Preprocessing Techniques
• Combining Neural Networks
• Pick the network with the “best fit”
• Average the network outputs
• Voting Scheme
Horizon Imaging, LLCInnovative Solutions in Image Processing
Voting Scheme to Combine NetworksNeural Net #1
Neural Net #2
Neural Net #N
1
2
N
Figure 3. Voting scheme to combine Neural Networks
Input Vector
yN
y2
y1
= i
yN > T
y2 > T
y1 > T
i =
0 for yi T
1 for yi > T
CombinedOutput
Horizon Imaging, LLCInnovative Solutions in Image Processing
Preprocessing Technique using Wavelets
• Coiflet wavelet
• Daubechies wavelet
• Haar wavelet (averages adjacent pixels)
Second-level wavelet approximation
Horizon Imaging, LLCInnovative Solutions in Image Processing
Image f(x,y)
Low
High
Low
High
Low
High
LL
LH
HL
HH
Horizontal filter Vertical filter
2
2
2
2
2
2
One-Level of a Wavelet Transform
Horizon Imaging, LLCInnovative Solutions in Image Processing
Third-level Wavelet Decomposition
HHLH
LL HL
Horizon Imaging, LLCInnovative Solutions in Image Processing
Test Case with Single Classifier
Output
Figure 7. Test case with single classifier
320x160 pixels
Wavelet Transform PCA
Neural Classifier
512 x 480 Image Image
Preparation
Horizon Imaging, LLCInnovative Solutions in Image Processing
Test Case with Multiple Classifiers
Image 1 Neural Classifier
Image N Neural Classifier
Combine Networks
Wavelet Transform
OutputImage Preparation
320 x 160 pixels
512 x 480 Image
Figure 8. Test case with multiple classifiers
Horizon Imaging, LLCInnovative Solutions in Image Processing
Test Cases
A. Coiflet 6-coefficient wavelet to 3 levels; 3rd level approximation image (40x20 pixels) and 3 sidebands form input to 4 neural networks with 800 inputs each.
B. Daubechies 6-coefficient wavelet to 3 levels; 3rd level approximation image (40x20) and 3 sidebands form input to 4 neural networks with 800 inputs each.
C. Coiflet 6-coefficient wavelet to 2 levels (80x40 pixels); 4 image zones fed to 4 separate neural networks with 800 inputs each.
Horizon Imaging, LLCInnovative Solutions in Image Processing
Test Cases
D. Daubechies 6-coefficient wavelet to 2 levels (80x40 pixels); 4 image zones fed to 4 separate neural networks with 800 inputs each.
E. Harr wavelet to 2 levels (80x40 pixels); 4 image zones fed to 4 separate neural networks with 800 inputs each.
F. Harr wavelet to 2 levels (80x40 pixels) and then PCA transform fed to a neural network with 512 inputs.
Horizon Imaging, LLCInnovative Solutions in Image Processing
Test Cases
G. Harr wavelet to 3 levels (40x20 pixels) fed to a neural network with 800 inputs.
H. Coiflet 6-coefficient wavelet to 1 level (160X80 = 12800 pixels). The first level approximation image is divided into 16 image zones (40x20 pixels per zone). The zones are fed into separate neural networks with 800 inputs each.
Horizon Imaging, LLCInnovative Solutions in Image Processing
0
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4
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16
18
ERR %
A B C D E F G H
False Rejects
Holdout Error
Training Error
Summary of Performance
Horizon Imaging, LLCInnovative Solutions in Image Processing
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