AdvNet: Adversarial Contrastive Residual Net for 1 Million Kinship Recognition

Kinship verification in the wild is an interesting and challenging problem. The goal of kinship verification is to determine whether a pair of faces are blood relatives or not. Most previous methods for kinship verification can be divided as hand-crafted features based shallow learning methods and convolutional neural network (CNN) based deep learning methods. Nevertheless, these methods are still posed with the challenging task of recognizing kinship cues from facial images. Part of the reason for this may be that, the family information and the distribution difference of pairwise kin-face data based kinship verification issue are rarely considered. Inspired by maximum mean discrepancy (MMD) and generative adversarial net (GAN), family ID based Adversarial contrastive residual Network (AdvNet) is proposed for large-scale (1 Million) kinship recognition in this paper. The MMD based adversarial loss (AL), pairwise contrastive loss (CL) and family ID based softmax loss (SL) are jointly formulated in the proposed AdvNet for kin-relation enhancement and discovery. Further, the deep nets ensemble is used for deep kin-feature augmentation. Finally, Euclidean distance metric is used for kinship recognition. Extensive experiments on the 1st Large-Scale Kinship Recognition Data Challenge (Families in the wild) show the effectiveness of our proposed AdvNet and ensemble based feature augmentation.