diff --git a/Dataset.mat b/Dataset.mat
new file mode 100644
index 0000000..55178d3
Binary files /dev/null and b/Dataset.mat differ
diff --git a/facedata.m b/facedata.m
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+++ b/facedata.m
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+clear all;
+close all;
+
+N = 512 ; % new image size
+
+%abubakr
+abubakr = image_input('a1.JPG',N);
+% hamdy
+hamdy = image_input('b1.JPG',N);
+% eslam
+eslam = image_input('c1.JPG',N);
+% weley
+welely = image_input('d1.JPG',N);
+% osama
+osama = image_input('e1.JPG',N);
+% wizo
+wizo = image_input('f1.JPG',N);
+
+%% store
+st.names = {'abubakr','hamdy','eslam','welely','osama','wizo'};
+st.data{1} = abubakr;
+st.data{2} = hamdy;
+st.data{3} = eslam;
+st.data{4} = welely;
+st.data{5} = osama;
+st.data{6} = wizo;
+
+data  = [abubakr hamdy eslam; welely osama wizo];
+figure
+imshow(data,'Initialmagnification','fit');title('Data set')
+
+save Dataset st;
\ No newline at end of file
diff --git a/facerecog.m b/facerecog.m
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+++ b/facerecog.m
@@ -0,0 +1,130 @@
+clear all;
+close all;
+
+load Dataset; %load the training set we work on
+
+M = 6; %number of dataset images
+N = 512; %new size of the dataset iamges
+
+%%plotting the training set
+%To_imshow = [ st.data{1}  st.data{2} st.data{3} ; st.data{4} st.data{5} st.data{6}];
+%figure
+%imshow(To_imshow,'Initialmagnification','fit')
+%title('Data set')
+%% compute mean
+
+average_image = zeros(N);
+for i = 1 : M
+    st.data{i} = im2single(st.data{i});
+    average_image = average_image + st.data{i};
+end
+average_image = average_image/M;
+% %plotting the average image
+% figure
+% imshow(average_image,'Initialmagnification','fit')
+% title('The dataset average')
+
+%% normalize (remove mean)
+
+for i = 1 : M
+    st.dataAvg{i}  = st.data{i} - average_image;
+end
+%%plotting the training set subtracted of the mean
+%To_imshow  = [ st.dataAvg{1}  st.dataAvg{2} st.dataAvg{3}; st.dataAvg{4} st.dataAvg{5} st.dataAvg{6}];
+%figure
+%imshow(To_imshow,'Initialmagnification','fit')
+%title('Dataset minus the average')
+
+
+%% generate A = [ img1(:)  img2(:) ...  imgM(:) ]
+
+A = zeros(N*N,M); % N^2*M
+for i=1:M
+    A(:,i) = st.dataAvg{i}(:);
+end
+% Covariance matrix in small dimension (transposed)
+C = A'*A; % M*M
+%%plotting the covariance matrix
+%figure
+%imagesc(C)
+%title('Covariance')
+
+%% eigen vectros  in small dimension
+
+[Veigvec,Deigval] = eig(C);% V(M*M), D(M*M) diagonal with M eigen values
+% the eigen face in large dimension  A*Veigvec is eigen vector of Clarge
+Vlarge = A*Veigvec; % [N^2*M] * [M*M] = N^2*M
+% reshape each column (N^2*1) to obtain each eigen face (N*N)
+eigenfaces = cell(1,M);
+for i=1:M
+    eigenfaces{i} = reshape(Vlarge(:,i),N,N);
+end
+lambdas = diag(Deigval); %obtaining eigen values
+[lambdas_sorted,lambdas_sorted_index]=sort(lambdas,'descend'); %sorting the eigen values in descending order
+% %plotting the eigenfaces (ghost faces) in descending order
+% To_imshow  = [ eigenfaces{lambdas_sorted_index(1)}  eigenfaces{lambdas_sorted_index(2)} eigenfaces{lambdas_sorted_index(3)}; eigenfaces{lambdas_sorted_index(4)} eigenfaces{lambdas_sorted_index(5)} eigenfaces{lambdas_sorted_index(6)} ];
+% figure
+% imshow(To_imshow,'Initialmagnification','fit')
+% title('eigenfaces')
+
+%% weights
+
+sel = M; % select number of eigen faces (max = M)
+wi = zeros(sel,M);
+for mi=1:M  % image number
+    for i=1:sel   % eigen face for coeff number
+        % we get the weights matrix (sel*M) by multiplying
+        % the transposed eigenfaces (1*N^2) by the corresponding images (N^2*1) 
+        wi(mi,i) = eigenfaces{lambdas_sorted_index(i)}(:)' * A(:,mi) ;
+    end
+end
+
+%% classify a new image
+
+%first the user enters the name of the test image
+testFace_name = input('Enter the test image name: ');
+testFace = imread(testFace_name);
+testFace = rgb2gray(testFace);
+testFace = imresize(testFace,[N N]);
+testFace = im2single(testFace);
+
+%figure
+%imshow(testFace,'Initialmagnification','fit')
+%title('Test face')
+
+%then we normalize the test face
+testFaceColumn = testFace(:)-average_image(:);
+
+%then we calculate the new weights of the new image
+wface = zeros(1,sel);
+for j = 1 : sel
+    wface(j)  =  eigenfaces{lambdas_sorted_index(j)}(:)' * testFaceColumn ;
+end
+
+
+% compute distance
+diffWeights = zeros(1,M);
+for mi = 1 : M
+    sumdist=0;
+    for j=1:sel
+        sumdist = sumdist + (wface(j) -wi(mi,j)).^2;
+    end
+    diffWeights(mi) = sqrt(sumdist);
+end
+
+% defining a threshold to the face classes
+% test image has to be frontal, bare headed, average brightness and white background
+% (i.e. passort image).
+theta = 2.75e+3;
+if min(diffWeights) > theta
+    fprintf('The image you have entered is of an UNKNOWN person\n');
+else
+    index = find(diffWeights == min(diffWeights));
+    person_detected_name = st.names{index};
+    fprintf('The person is %s \n',person_detected_name);
+    person_detected_image = st.data{index};
+    To_imshow = [testFace person_detected_image];
+    figure
+    imshow(To_imshow,'Initialmagnification','fit')
+    title('Testface ------------------------------------- Matched image')
+end
diff --git a/image_input.m b/image_input.m
new file mode 100644
index 0000000..c9e98d0
--- /dev/null
+++ b/image_input.m
@@ -0,0 +1,7 @@
+function y = image_input(image_name, N)
+%This function takes the image name, converts it to gray and resize it to NXN. 
+y = imread(image_name);
+y = rgb2gray(y);
+y = imresize(y,[N N]);
+end
+
diff --git a/word.m b/word.m
new file mode 100644
index 0000000..f3e652a
--- /dev/null
+++ b/word.m
@@ -0,0 +1,115 @@
+clear all;
+close all;
+
+load Dataset; %load the training set we work on
+
+M = 6; %number of dataset images
+N = 512; %new size of the dataset iamges
+
+%plotting the training set
+To_imshow = [ st.data{1}  st.data{2} st.data{3} ; st.data{4} st.data{5} st.data{6}];
+figure
+imshow(To_imshow,'Initialmagnification','fit')
+title('Data set')
+%% compute mean
+
+average_image = zeros(N);
+for i = 1 : M
+    st.data{i} = im2single(st.data{i});
+    average_image = average_image + st.data{i};
+end
+average_image = average_image/M;
+
+%% normalize (remove mean)
+
+for i = 1 : M
+    st.dataAvg{i}  = st.data{i} - average_image;
+end
+
+%% generate A = [ img1(:)  img2(:) ...  imgM(:) ]
+
+A = zeros(N*N,M); % N^2*M
+for i=1:M
+    A(:,i) = st.dataAvg{i}(:);
+end
+% Covariance matrix in small dimension (transposed)
+C = A'*A; % M*M
+
+%% eigen vectros  in small dimension
+
+[Veigvec,Deigval] = eig(C);% V(M*M), D(M*M) diagonal with M eigen values
+% the eigen face in large dimension  A*Veigvec is eigen vector of Clarge
+Vlarge = A*Veigvec; % [N^2*M] * [M*M] = N^2*M
+% reshape each column (N^2*1) to obtain each eigen face (N*N)
+eigenfaces = cell(1,M);
+for i=1:M
+    eigenfaces{i} = reshape(Vlarge(:,i),N,N);
+end
+lambdas = diag(Deigval); %obtaining eigen values
+[lambdas_sorted,lambdas_sorted_index]=sort(lambdas,'descend'); %sorting the eigen values in descending order
+%plotting the eigenfaces (ghost faces) in descending order
+To_imshow  = [ eigenfaces{lambdas_sorted_index(1)}  eigenfaces{lambdas_sorted_index(2)} eigenfaces{lambdas_sorted_index(3)}; eigenfaces{lambdas_sorted_index(4)} eigenfaces{lambdas_sorted_index(5)} eigenfaces{lambdas_sorted_index(6)} ];
+figure
+imshow(To_imshow,'Initialmagnification','fit')
+title('eigenfaces')
+
+%% weights
+
+sel = M; % select number of eigen faces (max = M)
+wi = zeros(sel,M);
+for mi=1:M  % image number
+    for i=1:sel   % eigen face for coeff number
+        % we get the weights matrix (sel*M) by multiplying
+        % the transposed eigenfaces (1*N^2) by the corresponding images (N^2*1) 
+        wi(mi,i) = eigenfaces{lambdas_sorted_index(i)}(:)' * A(:,mi) ;
+    end
+end
+
+%% classify a new image
+
+%first the user enters the name of the test image
+testFace_name = input('Enter the test image name: ');
+testFace = imread(testFace_name);
+testFace = rgb2gray(testFace);
+testFace = imresize(testFace,[N N]);
+testFace = im2single(testFace);
+
+%plotting the input test face
+figure
+imshow(testFace,'Initialmagnification','fit')
+title('Test face')
+
+%then we normalize the test face
+testFaceColumn = testFace(:)-average_image(:);
+
+%then we calculate the new weights of the new image
+wface = zeros(1,sel);
+for j = 1 : sel
+    wface(j)  =  eigenfaces{lambdas_sorted_index(j)}(:)' * testFaceColumn ;
+end
+
+
+% compute distance
+diffWeights = zeros(1,M);
+for mi = 1 : M
+    sumdist=0;
+    for j=1:sel
+        sumdist = sumdist + (wface(j) -wi(mi,j)).^2;
+    end
+    diffWeights(mi) = sqrt(sumdist);
+end
+
+% defining a threshold to the face classes
+theta = 5.4e+3;
+if min(diffWeights) > theta
+    fprintf('The image you have entered is of an UNKNOWN person\n');
+else
+    index = find(diffWeights == min(diffWeights));
+    person_detected_name = st.names{index};
+    fprintf('The person is %s \n',person_detected_name);
+    person_detected_image = st.data{index};
+    To_imshow = [testFace person_detected_image];
+    figure
+    imshow(To_imshow,'Initialmagnification','fit')
+    title('Testface ------------------------------------- Matched image')
+end