Shahab Anbarjafari

It was interesting :) quite useful (at least for me)

:)

:)

after reading cmments, I don,t know I should use it or not!

first of all, I couldn't run the file because it doesn't have the files it needs

I read the code it is not LDA, it's PCA because you did the following:
1- loaded the pictures for training in one matrix as rows (face space)
2- loaded the rest of the pictures for test in another matrix
3- subtracted mean face from all of face space (zero mean face space)
4- covariance for zero mean face space
5- eigenvectors for covariance matrix
6- project the eigenvectors on every picture in train and test sets
7-measure the euclidean distance for test set
 
that is PCA

for LDA, you must get mean for each class
then the mean of all the classes
calculate within-class scatter matrix
calculate the between-class scatter matrix
then the eigenvectors for the product and so on

which you didn't do