IMAGE PRE-PROCESSING FOR CLASSIFICATION (BIOMETRIC IDENTIFICATION) BY A NEURAL NETWORK Anthony Vannelli, Steve Wagner, and Ken McGarvey Horizon Imaging, LLC Horizon Imaging, LLC Innovative 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, LLC Innovative 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, LLC Innovative Solutions in Image Processing

Biometric Identification Region of Interest 320x160 = 51,200 pixels Horizon Imaging, LLC Innovative Solutions in Image Processing

Preprocessing Techniques Non-parametric “Holistic” Data-driven No Hand Geometry No Fidiucial Points Horizon Imaging, LLC Innovative 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, LLC Innovative Solutions in Image Processing

Preprocessing Techniques Combining Neural Networks Pick the network with the “best fit” Average the network outputs Voting Scheme Horizon Imaging, LLC Innovative Solutions in Image Processing

Voting Scheme to Combine Networks Horizon Imaging, LLC Innovative Solutions in Image Processing

Preprocessing Technique using Wavelets Coiflet wavelet Daubechies wavelet Haar wavelet (averages adjacent pixels) Second-level wavelet approximation Horizon Imaging, LLC Innovative Solutions in Image Processing

Image f(x,y) Low High Low High Low High LL LH HL HH Horizontal filterVertical filter One-Level of a Wavelet Transform Horizon Imaging, LLC Innovative Solutions in Image Processing

Third-level Wavelet Decomposition HHLH LLHL Horizon Imaging, LLC Innovative Solutions in Image Processing

Test Case with Single Classifier Output Figure 7. Test case with single classifier 320x160 pixels Wavelet TransformPCA Neural Classifier 512 x 480 Image Image Preparation Horizon Imaging, LLC Innovative Solutions in Image Processing

Test Case with Multiple Classifiers Image 1Neural Classifier Image NNeural Classifier Combine Networks Wavelet Transform Output Image Preparation 320 x 160 pixels 512 x 480 Image Figure 8. Test case with multiple classifiers Horizon Imaging, LLC Innovative Solutions in Image Processing

Test Cases A.Coiflet 6-coefficient wavelet to 3 levels; 3 rd 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; 3 rd 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, LLC Innovative 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, LLC Innovative 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 = 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, LLC Innovative Solutions in Image Processing

Summary of Performance Horizon Imaging, LLC Innovative Solutions in Image Processing