Abstract

This paper presents a new discriminative deep metriclearning (DDML) method for face verification in the wild. Different from existing metric learning-based face verifica-tion methods which aim to learn a Mahalanobis distancemetric to maximize the inter-class variations and minimize the intra-class variations, simultaneously, the proposed D-DML trains a deep neural network which learns a set of hierarchical nonlinear transformations to project face pairs into the same feature subspace, under which the distance of each positive face pair is less than a smaller threshold and that of each negative pair is higher than a larger threshold,respectively, so that discriminative information can be exploited in the deep network. Our method achieves very com-petitive face verification performance on the widely usedLFW and YouTube Faces (YTF) datasets.