This is not going to work (at least currently). Features for faces are not yet powerful enough to get you to a reasonable set of matches without also missing th

This is not going to work (at least currently). Features for faces are not yet powerful enough to get you to a reasonable set of matches without also missing the right face in a large percentage of cases. Variations due to lighting, pose, and expression end up causing the same person to look very different -- often to the point where two different people in the same configuration can look more similar than the same person in different configurations. That being said, if the database is small enough (e.g., in a limited scenario or by applying other non-vision filters first), then the state of the art methods do use a similar approach. However, in practice people use methods with much stronger priors than KNN. Because faces fall into a low-dimensional manifold[1], you can take advantage of that to constrain queries much more than with generic KNN. [1] My PhD advisor has published several works about this topic, e.g., http://vision.ucsd.edu/kriegman-grp/papers/ijcv98.pdf http://vision.ucsd.edu/kriegman-grp/papers/ijcv98.pdf http://virtualhost.cs.columbia.edu/~belhumeur/journal/invariance_v2.pdf http://virtualhost.cs.columbia.edu/~belhumeur/journal/invari... http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.123.3344&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.123...