Using non-negative matrix factorization (NMF) for facial recognition

Hi all,

I'm interested to find out how to implement NMF for facial recognition. I understand that the NMF works by taking V, which is a matrix of face images (n resolution x m persons), and factorize V = WH, where we get r basis vectors. I presume that this step is done using the training set. But I don't know how to proceed from here.

With PCA, after building the eigenfaces from the training set, we project the test image onto the face-space, then classify the image by comparing the weight coefficients between the test image and training images. But what about NMF? Do I use the basis vectors? What do I do with them?

Thank you.