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HAND GESTURE RECOGNITIO

using Mat

Artificial Intelligence PL57

Guide :Mr. S Baskar

Project Members1026 Tarun

1027 Sunil1030 Atul

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Introduction

Hand Gesture Recognition is very promising arePattern Recognition, it includes both Vision Gesture Recognition as well as Sensor BRecognition.

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Hand Gesture Recognition

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Domain Explanation

American Sign Language is a HandGesture Language which is used byDeaf & Dumb people through outthe world. It contains differentgestures for each letter of theEnglish Alphabet.

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Data Collection

Source of DataInternet

Images from Camera

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Feed Forward Neural NetworkALGORITHM USED6

Hand Gesture Recognition

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Feed Forward Neural Network7

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Feed Forward Neural Network

Hand Gesture Recognition

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Initialization

Output

Blame

Adjust Weight

Initialize Weights randomly initialize bias

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Feed Forward Neural Network

Hand Gesture Recognition

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Initialization

Output

Blame

Adjust Weight

Calculate Output for the entire netusing Activation Function

Output=A( w k*Ik + bias )

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Feed Forward Neural Network

Hand Gesture Recognition

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Initialization

Output

ErrorAdjust Weight

Calculate Error for NeuronsSum Square Error

Mean Square Error

Calculate Blame for the each neuro

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Feed Forward Neural Network

Hand Gesture Recognition

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Initialization

Output

Blame

Adjust Weight

Adjust Weights using Back Propagand Gradient Descent

Wij = W ij + r * E i * A j ( Ij ) * O i

r Learning RateEj Blame for Neuron JAj Derivative of Neuron Js ActIj Input in previous stepO j Output in previous step

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Working of Software12

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Flow Chart

Hand Gesture Recognition

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Training SetImages &

Target Values

PreprocessImages

Train NeuralNetwork

Test ImagePreprocess

ImageMaO

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Pre-Processing

CaptureRGB 2 GRAY

Resize

Contrast Reset

X-Y Gradient

Orienting

FeatureVector

Images were takenfrom each specifiedfolders using loop

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Pre-Processing

Capture

RGB 2 GRAY

Resize

Contrast Reset

X-Y Gradient

Orienting

FeatureVector

Each and everyimage was convertedinto grayscale usingrgb2gray function

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Pre-Processing

Capture

RGB 2 GRAY

ResizeContrast Reset

X-Y Gradient

Orienting

FeatureVector

Images of differentsizes were convertedto common size of80x60

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Pre-Processing

Capture

RGB 2 GRAY

Resize

Contrast Reset

X-Y Gradient

Orienting

FeatureVector

Darken Background

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Pre-Processing

Capture

RGB 2 GRAY

Resize

Contrast Reset

X-Y Gradient

Orienting

FeatureVector

Edge DetectionEdges in X-Direction

Edges in Y-Direction

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Pre-Processing

Capture

RGB 2 GRAY

Resize

Contrast Reset

X-Y Gradient

OrientingFeatureVector

Find Edge DirectionsOrientation liesbetween -180 to 180

degrees

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Pre-Processing

Capture

RGB 2 GRAY

Resize

Contrast Reset

X-Y Gradient

Orienting

FeatureVector

Finally Image isconverted in ColumnVector of Degrees

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Training Neural Network

Following Code was used to train Neural Networ

net=newff (minmax(x),[30 target_neurons],{'logsig','logsig'},'tranet=train(net,x,t);

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Testing

Hand Gesture Recognition

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Testing is simple, Image is taken from Camera anthen pre-processed and given to neural network. the basis of output vector, Target image is shown

sim (net,test)

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THANK YOU25

Hand Gesture Recognition