We can now calculate how similar this reconstructed face is to the input face by using the same getSimilarity() function we created previously for comparing two images, where a value less than 0.3 implies that the two images are very similar. For Eigenfaces, there is one eigenvector for each face, so reconstruction tends to work well and therefore we can typically use a threshold of 0.5, but Fisherfaces has just one eigenvector for each person, so reconstruction will not work as well and therefore it needs a higher threshold, say 0.7. This is done as follows: