Abstract:
High-fidelity kinship face synthesis receives increasing interest in this technology for visual kinship applications, including law enforcement, social media analysis, fi...Show MoreMetadata
Abstract:
High-fidelity kinship face synthesis receives increasing interest in this technology for visual kinship applications, including law enforcement, social media analysis, finding lost children, etc. However, it is a challenging task because of the unresolved ambiguities by the limited amount of available kinship data and severe data noise. To address these issues, we leverage the pretrained state-of-the-art face synthesis model, StyleGAN2, to assist the synthesis. With StyleGAN2, we develop three different kinship face synthesis strategies: (1) synthesis based on the kinship statistics, (2) synthesis using the latent code interpolation of the parents, and (3) synthesis based on the latent code interpolation from the disentangled age and gender independent latent space. The first two methods synthesize kinship faces through the direct manipulation of the original StyleGAN2 latent codes. The third one, on the other hand, is a two-stage synthesis method which first learns an age and gender invariant latent representation upon the one of StyleGAN2 to represent the genes. Combining with the maximal selection process to fuse the corresponding representation of parents, we form the genes of the child followed by them feeding back to StyleGAN2 for the final synthesis. With extensive ablation studies and experiments, we observe that all three methods can generate more photo-realistic and clearer faces than the previous state-of-the-art method. In addition, the third method achieves the best kinship verification results on the FIW dataset. Surprisingly, the subjective evaluation results of the three proposed methods are very close because typical humans are not good at recognizing unfamiliar kinship faces.
Published in: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
Date of Conference: 15-18 December 2021
Date Added to IEEE Xplore: 12 January 2022
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