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Hand gesture recognition using Deep learning

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Shweta Saboo
Shweta Saboo on 13 Jan 2021
Commented: Shweta Saboo on 1 Feb 2021
I have extracted feature matrix for hand gestures. How can recognition be done using Deep learning with input as the feature matrix?

Answers (3)

Raynier Suresh
Raynier Suresh on 18 Jan 2021
If you have a data set of numeric features, then you can train a deep learning network using a feature input layer. The below code is a simple example on how to use the feature input layer.
XTrain = [0 0;0 1;1 0;1 1]; % Input Features (Number of Observations x Number of Features)
YTrain = categorical({'Action1';'Action2';'Action2';'Action3'}); % Output Labels for each observation
numClasses = numel(categories(YTrain));
numFeatures = size(XTrain,2);
layers = [
featureInputLayer(numFeatures)
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer]; % Define the Layers
options = trainingOptions('sgdm');
net = trainNetwork(XTrain,YTrain,layers,options); % Train the network
classify(net,[0 1])
Refer the below link for more information:
  3 Comments

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Raynier Suresh
Raynier Suresh on 25 Jan 2021
For a deep learning network every input image is considered as a matrix of numbers, So in place of an image you can also feed your feature matrix and train the network only things is the feature matrix must to reshaped to a proper size so that the imageInputLayer accepts it. The below code will give you an example
XTrain = [0 0;0 1;1 0;1 1]; % Input Features (Number of Observations x Number of Features)
XTrain = reshape(XTrain',[1 2 1 4]); % Reshape the XTrain (1 x Number of Features x 1 x Number of Observation)
YTrain = categorical({'Action1';'Action2';'Action2';'Action3'}); % Output Labels for each observation
options = trainingOptions('sgdm','MaxEpochs',150);
inputSize = [1 2 1]; % set the input size as (1 x Number of Features x 1)
outputSize = numel(categories(YTrain)); % Number of output categories
layers = [imageInputLayer(inputSize);fullyConnectedLayer(outputSize);softmaxLayer;classificationLayer];
net = trainNetwork(XTrain,YTrain,layers,options); % Train the network
classify(net,[1 1])
  2 Comments
Raynier Suresh
Raynier Suresh on 28 Jan 2021
Check whether you have changed the input size of data you fed into the classify function. I have modified the same code for your input size.
XTrain = rand(100,20); % Input Features (Number of Observations x Number of Features)
XTrain = reshape(XTrain',[1 20 1 100]); % Reshape the XTrain (1 x Number of Features x 1 x Number of Observation)
YTrain = categorical(randi(10,[1,100])'); % Output Labels for each observation
options = trainingOptions('sgdm','MaxEpochs',150);
inputSize = [1 20 1]; % set the input size as (1 x Number of Features x 1)
outputSize = numel(categories(YTrain)); % Number of output categories
layers = [imageInputLayer(inputSize);fullyConnectedLayer(outputSize);softmaxLayer;classificationLayer];
net = trainNetwork(XTrain,YTrain,layers,options); % Train the network
classify(net,rand(1,20))

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Shweta Saboo
Shweta Saboo on 29 Jan 2021
Thank you so much ,it worked.
  1 Comment
Shweta Saboo
Shweta Saboo on 1 Feb 2021
After classification, I am trying to calculate the recognition accuracy , but in the above code test cases are not defined and hence recognition accuracy cannot be calculated.

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