Using non-negative matrix factorization (NMF) for facial recognition 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?
One major issue with this approach is that if you represent your image as a vector of pixel intensities, you have zero translation-invariance.  Two pictures that are exactly the same but shifted over 10 pixels will likely look like dissimilar vectors.  That is why people try to use other methods that handle translation, like convolutional neural networks or SIFT features.