Multi-scale deep relational reasoning for facial kinship verification

Abstract

In this paper, we propose a deep relational network which exploits multi-scale information of facial images for kinship verification. Unlike most existing deep learning based facial kinship verification methods which employ convolutional neural networks to extract holistic features, we present a deep model to exploit facial kinship relationship from local regions. For each given pair of face images, our method uses two convolutional neural networks which share parameters to extract different scales of features, which are expected to provide global contextual information of face images. We split a set of features at the same scale into multiple groups, where different groups capture information of different local regions. For each pair of local feature groups which are extracted from the same scale and position, we propose a relation network to reason their relationship, and use a verification network to infer the kin relation based on the results of local relations from different facial regions. We conduct experiments on two widely used facial kinship datasets: KinFaceW-I and KinFaceW-II, and our experimental results are presented to demonstrate the effectiveness of our approach.