FamilyGAN: Generating Kin Face Images Using Generative Adversarial Networks

Abstract

Automatic kinship verification using face images involves analyzing features and computing similarities between two input images to establish kin-relationship. It has gained significant interest from the research community and several approaches including deep learning architectures are proposed. One of the law enforcement applications of kinship analysis involves predicting the kin image given an input image. In other words, the question posed here is: “given an input image, can we generate a kin-image?” This paper attempts to generate kin-images using Generative Adversarial Learning for multiple kin-relations. The proposed FamilyGAN model incorporates three information, kin-gender, kinship loss, and reconstruction loss, in a GAN model to generate kin images. FamilyGAN is the first model capable of generating kin-images for multiple relations such as parent-child and siblings from a single model. On the WVU Kinship Video database, the proposed model shows very promising results for generating kin images. Experimental results show 71.34% kinship verification accuracy using the images generated via FamilyGAN.