Hand gesture recognition using Deep learning

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Shweta Saboo
Shweta Saboo 2021-1-13
评论： Shweta Saboo 2021-2-1
I have extracted feature matrix for hand gestures. How can recognition be done using Deep learning with input as the feature matrix?

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回答（3 个）

Raynier Suresh
Raynier Suresh 2021-1-18
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:
Train Network with Numeric Features : https://www.mathworks.com/help/deeplearning/ref/trainnetwork.html?s_tid=doc_ta#mw_fcd828cc-de80-4922-8cc2-806c07deb31a
Raynier Suresh
Raynier Suresh 2021-1-25
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 个评论
Shweta Saboo
Shweta Saboo 2021-1-28
Thanks for the response. I am having 100 observations with 20 features for each observation . When I am trying to run the above code, following error is occuring:
"Error using DAGNetwork/calculatePredict>predictBatch (line 151)
Incorrect input size. The input images must have a size of [1 20 1]."
Please suggest.
Raynier Suresh
Raynier Suresh 2021-1-28
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 2021-1-29
Thank you so much ,it worked.
  1 个评论
Shweta Saboo
Shweta Saboo 2021-2-1
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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