Adv-Kin: An Adversarial Convolutional Network for Kinship Verification

Abstract

Kinship verification in the wild is an interesting and challenging problem, which aims to determine whether two unconstrained facial images are from the same family. Most previous methods for kinship verification can be divided as low-level hand-crafted features based shallow methods and kin data trained generic convolutional neural network (CNN) based deep methods. Nevertheless, these general methods cannot well mining the potential information implied in kin-relation data. Inspired by MMD and GAN, Adv-Kin method is proposed in this paper. The discrimination of deep features can be improved by introducing MMD loss (ML) to minimize the distribution difference between parents domain and children domain. In addition, we propose the adversarial loss (AL) that can further improve the robustness of CNN model. Extensive experiments on the benchmark KinFaceW-I, KinFaceW-II, Cornell KinFace and UB KinFace show promising results over many state-of-the-art methods.