Learning weighted similarity measurements for unconstrained face recognition

Unconstrained face recognition is the problem of deciding if an image pair is showing the same individual or not, without having class specific training material or knowing anything about the image conditions. In this paper, an approach of learning suited similarity measurements is introduced. For this the image is partitioned into several parts, to extract image region based histograms of gradients, local binary patterns and three patch local binary patterns. The similarities of respective patches are computed and it is learnt how to weight the different image regions. Finally, a fusion is applied using a multilayer perceptron. Evaluations are done on the ¿labeled faces in the wild¿ dataset.