image pre-processing for classification (biometric identification) by a neural network anthony...

IMAGE PRE-PROCESSING FOR CLASSIFICATION (BIOMETRIC IDENTIFICATION)

BY A NEURAL NETWORK

Anthony Vannelli, Steve Wagner, and Ken McGarveyHorizon Imaging, LLC

email: [email protected]

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


• 512x480 raw image or 245,760 inputs to network

• Large neural network

• Poor classification performance

• Slow convergence

Curse of dimensionality


Biometric Identification

Region of Interest

320x160 = 51,200 pixels


Preprocessing Techniques

• Non-parametric

• “Holistic”

• Data-driven

• No Hand Geometry

• No Fidiucial Points



• 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



• Combining Neural Networks

• Pick the network with the “best fit”

• Average the network outputs

• Voting Scheme


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


Preprocessing Technique using Wavelets

• Coiflet wavelet

• Daubechies wavelet

• Haar wavelet (averages adjacent pixels)

Second-level wavelet approximation


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


Third-level Wavelet Decomposition

HHLH

LL HL


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


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


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.


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.


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.


0

2

4

6

8

10

12

14

16

18

ERR %

A B C D E F G H

False Rejects

Holdout Error

Training Error

Summary of Performance


image pre-processing for classification (biometric identification) by a neural network anthony...

Documents