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Visual kinship recognition aims to identify blood relatives from facialimages. Its practical application-- like in law-enforcement, videosurveillance, automatic family album management, and more-- has motivated manyresearchers to put forth effort on the topic as of recent. In this paper, wefocus on a new view of visual kinship technology: kin-based face generation.Specifically, we propose a two-stage kin-face generation model to predict theappearance of a child given a pair of parents. The first stage includes a deepgenerative adversarial autoencoder conditioned on ages and genders to mapbetween facial appearance and high-level features. The second stage is ourproposed DNA-Net, which serves as a transformation between the deep and geneticfeatures based on a random selection process to fuse genes of a parent pair toform the genes of a child. We demonstrate the effectiveness of the proposedmethod quantitatively and qualitatively: quantitatively, pre-trained models andhuman subjects perform kinship verification on the generated images ofchildren; qualitatively, we show photo-realistic face images of children thatclosely resemble the given pair of parents. In the end, experiments validatethat the proposed model synthesizes convincing kin-faces using both subjectiveand objective standards.

What Will Your Child Look Like? DNA-Net: Age and Gender Aware Kin Face Synthesizer