Hi Yusuf,
I understand that you want to process an input image to compare face datasets using PCA. You can compare the input image with your existing face dataset processed using PCA, for that you need to ensure that the input image undergoes the same preprocessing and PCA transformation as your dataset.
Preprocess the input image:
- Resize the image
- Convert to grayscale
- Flatten the image
- Normalize the image
I wrote an example code to apply PCA to your input image:
% Load or define your PCA components and mean
% Assuming 'coeff' is your PCA coefficients matrix
% and 'meanImage' is the mean image from your dataset
% Read and preprocess the input image
inputImage = imread('path_to_input_image');
inputImage = imresize(inputImage, [height, width]); % Resize to match dataset
if size(inputImage, 3) == 3
inputImage = rgb2gray(inputImage); % Convert to grayscale if needed
end
% Flatten and normalize the input image
inputImageVector = double(inputImage(:)); % Flatten to a vector
inputImageVector = inputImageVector - meanImage; % Subtract the mean
% Project the input image onto the PCA space
transformedImage = coeff' * inputImageVector;
% Assuming 'pcaDataset' contains PCA-transformed dataset images
distances = zeros(size(pcaDataset, 2), 1);
for i = 1:size(pcaDataset, 2)
distances(i) = norm(transformedImage - pcaDataset(:, i));
end
% Find the closest match
[~, closestIndex] = min(distances);
disp(['The closest match is at index: ', num2str(closestIndex)]);
Hope this helps!
