DNA-Net: Age and Gender Aware Kin Face Synthesizer
Pengyu Gao, Joseph P. Robinson, Jiaxuan Zhu, Chao Ying Xia, Ming Shao, Siyu Xia · 2021
Visual kinship verification aims to detect blood relatives in facial images. Its practical application have motivated many researchers to focus on the topic as of recent. In this paper, we focus on a new view of visual kinship technology: kin-based face generation. Specifically, we propose a two-stage kin-face generation model to predict the appearance of a child given a pair of parents. The first stage includes a deep generative adversarial auto-encoder conditioned on ages and genders to map between facial appearance and high-level features. The second stage is our proposed DNA-Net, which serves as a transformation between the deep and genetic features based on a random selection process to fuse genes of a parent pair to form the genes of a child. We demonstrate the effectiveness of the proposed method quantitatively and qualitatively. Experiments validate that the proposed model synthesizes convincing kin-faces using both subjective and objective standards.