Error using extractLBP​Features>p​arseInputs Expected a string scalar or character vector for the parameter name.

I have faced this error whle I was using extractLBPFeatures:

Error using extractLBPFeatures>parseInputs

Expected a string scalar or character vector for the parameter name.

Error in extractLBPFeatures (line 10)

params = parseInputs(I,varargin{:});

Error in face_recognition_model (line 52)

testFeatures{i} = extractLBPFeatures(img, numNeighbors, radius, numBins);

Code:

%Face Recognition using LRR

%% Load a dataset of grayscale face images

Dataset = imageDatastore('ExtendedYaleB', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');

%% Split the data into training and testing sets

[trainImgs, testImgs] = splitEachLabel(Dataset, 0.7, 'randomized');

%% Extract local patches from the training images using the extractLBPFeatures function

numNeighbors = 8;

radius = 1;

numBins = numNeighbors*(numNeighbors-1)+3;

trainFeatures = cell(numel(trainImgs.Files),1);

for i = 1:numel(trainImgs.Files)

img = readimage(trainImgs,i);

trainFeatures{i} = LBPFeatures(img, numNeighbors, radius, numBins);

end

%% Train the local ridge regression model using the fitrlinear function and local ridge regression

lambda = 1;

span = 0.5;

for i = 1:numel(trainImgs.Files)

features = trainFeatures{i};

label = double(trainImgs.Labels(i));

idx = setdiff(1:numel(trainFeatures),i);

neighbors = vertcat(trainFeatures{idx});

neighborLabels = double(trainImgs.Labels(idx));

mdl = fitrlinear(neighbors,neighborLabels,'Learner','leastsquares','Lambda',lambda);

yhat = zeros(size(features));

for j = 1:size(features,1)

patch = features(j,:);

pred = predict(mdl,patch);

dist = pdist2(patch,neighbors);

w = exp(-dist.^2/(2*span^2));

yhat(j) = sum(w.*pred)/sum(w);

end

trainFeatures{i} = yhat;

end

trainFeatures = cell2mat(trainFeatures);

%% Train the linear regression model on the modified LBP features

mdl = fitrlinear(trainFeatures,double(trainImgs.Labels),'Learner','leastsquares','Lambda',lambda);

%% Save the model to a file

save('face_recognition_model.mat', 'mdl');

%% Extract local patches from the testing images and make predictions using the predict function

testFeatures = cell(numel(testImgs.Files),1);

for i = 1:numel(testImgs.Files)

img = readimage(testImgs,i);

testFeatures{i} = extractLBPFeatures(img, numNeighbors, radius, numBins);

end

testFeatures = cell2mat(testFeatures);

predictions = predict(mdl,testFeatures);

%% Evaluate the performance of the model using the confusionmat and classificationReport functions

confMat = confusionmat(testImgs.Labels,predictions);

classificationReport = classificationReport(testImgs.Labels,predictions);

%% Load the saved model from a file

load('face_recognition_model.mat');

%% Use the loaded model for prediction

testImg = imread('test_image.jpg');

testFeatures = extractLBPFeatures(testImg, numNeighbors, radius, numBins);

prediction = predict(mdl,testFeatures);