What is the recognition rate of PCA eigenfaces?

Question

I used the Database of Faces (formally the ORL Database) from the AT&T Laboratories Cambridge. The database consists of 400 images with 10 images per person, i.e, there is 10 images of each 40 person.

I separated 5 images of each person for training and the remaining 5 images of each person for testing.

So I have 2 folders:
1) Training (5 images/person = 200 images)
2) Testing (5 images/person = 200 images)
The photos in the training folder are different from those in the testing folder.

The percentage recognition rate I got is only 80%. But if I pre-process the image before recognition I got:
pre-processing with imajust: 82%
pre-processing with sharpen: 83%
pre-processing with sharpen and imadjust: 84%
(If pre-processing is done, it is applied to bot training and testing images)

For the number of eigenfaces used, all eigenvalues of matrix L are sorted and those who are less than a specified threshold, are eliminated.

L_eig_vec = [];
for i = 1 : size(V,2) 
    if( D(i,i)>1 )
        L_eig_vec = [L_eig_vec V(:,i)];
    end
end

I use matlab to implement the face recognition system. Is it normal that the recognition rate is that low?

Answer 1

The accuracy would depend on the classifier you are using once you have the data in the PCA projected space. In the original Turk/Pentland eigenface paper

http://www.face-rec.org/algorithms/PCA/jcn.pdf

they just use kNN / Euclidean distance but a modern implementation might use SVMs e.g. with an rbf kernel as the classifier in the "face space", with C and gamma parameters optimized using a grid search. LibSVM would do that for you and there is a Matlab wrapper available.

Also you should register the faces first i.e. warping the images so they have facial landmarks e.g. eyes, nose, mouyth in a harmonised position across all the dataset? If the images aren't pre-registered then you will get a performance loss. I would expect a performance in the 90s for a dataset of 5 people using Eigenfaces with SVM and pre-registration. That figure is a gut feeling based on prior implementation / performance of past student projects. One thing to note however is your number of training examples is very low - 5 points in a high dimensional space is not much to train a classifier on.