Effacer les filtres
Effacer les filtres

How can i combine gabor filters and CNN for face recognition

15 vues (au cours des 30 derniers jours)
Samia Cherif
Samia Cherif le 21 Juin 2023
Commenté : Samia Cherif le 1 Juil 2023
Dear matlab team
Can any one tell me how to feed the convolutional neural network with gabor filters
The best regards
and have a great day
  1 commentaire
Samia Cherif
Samia Cherif le 1 Juil 2023
Many thanks
but I should modify the inputlayer input sizes to accept the gabor features as inputs in the place of the input image
I hope this works
best regards

Connectez-vous pour commenter.

Réponse acceptée

ProblemSolver
ProblemSolver le 27 Juin 2023
Hello Samia
We don't have have the specifics of your needs. But you can apply the CNN with Gabor Filter by following steps:
  • Generate Gabor filter bank: Create a set of Gabor filters with different orientations and scales. Each filter in the bank represents a specific orientation and scale combination. Something like this:
% Step 1: Create Gabor filters
gaborSize = 15;
gaborOrientation = [0 45 90 135];
gaborFilters = cell(length(gaborOrientation), 1);
for i = 1:length(gaborOrientation)
gaborFilters{i} = gabor(gaborSize, gaborOrientation(i));
end
  • Prepare the training dataset: You need to have a set of training images that will be used to train your CNN. Make sure the images are properly labeled with their corresponding classes. make sure to replace 'path_to_training_images' with the actual path to your training image dataset.
% Step 2: Load and preprocess training images
trainingImages = imageDatastore('path_to_training_images', 'LabelSource', 'foldernames');
% Perform any necessary preprocessing on the training images
  • Preprocess the training dataset: Now you have the dataset, Before feeding the Gabor filters to the CNN, it's a good practice to preprocess the training images. Preprocessing steps may include resizing, normalization, or any other transformations required to enhance the training data.
  • Apply the Gabor filters to the training images: For each training image, you will then apply the Gabor filters to extract the features. This can be done by convolving the Gabor filters with the training images using the conv2 function in MATLAB. The resulting feature maps will represent the responses of the Gabor filters to the input images.
% Step 3: Apply Gabor filters to training images
numImages = numel(trainingImages.Files);
gaborResponses = zeros(gaborSize, gaborSize, length(gaborOrientation), numImages);
for i = 1:numImages
image = readimage(trainingImages, i);
for j = 1:length(gaborOrientation)
gaborResponse = conv2(image, gaborFilters{j}, 'same');
gaborResponses(:, :, j, i) = gaborResponse;
end
end
  • Train the CNN: Once you have the Gabor filter responses as feature maps, you can use them to train your CNN. You can use MATLAB's Deep Learning Toolbox to define and train your CNN model. This typically involves defining the network architecture, specifying the layers, setting the hyperparameters, and training the network using the feature maps as input.
% Step 4: Train the CNN using the Gabor filter responses as features
layers = [
% Define your CNN architecture here
% Include layers like convolutional, pooling, and fully connected layers
];
% Specify the training options
options = trainingOptions('sgdm', 'MaxEpochs', 10, 'MiniBatchSize', 32, 'Plots', 'training-progress');
% Train the CNN using the Gabor filter responses
trainedNet = trainNetwork(gaborResponses, trainingImages.Labels, layers, options);
Adjust the parameters like gaborSize, gaborOrientation, and the CNN architecture according to your specific requirements.
I hope this helps.

Plus de réponses (0)

Produits


Version

R2018a

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by