CFR-GAN: A Generative Model for Craniofacial Reconstruction

Pengyue Lin, Wen Yang, Siyuan Xia, Yu Jiang, Xiaoning Liu, Guohua Geng

Abstract: Craniofacial reconstruction is to reconstruct the face from the skull based on the relationship between the skull and the face to help recognition. This paper proposes a deep generative model for craniofacial reconstruction: CFR-GAN, which avoids the disadvantages of traditional methods of insufficient deep information learning ability of craniofacial data and insufficient ability to express specific features of the dataset. The model is divided into two steps: rough reconstruction and refinement reconstruction. Rough reconstruction rebuilds the overall structural content of the corresponding human head through the skull, and refinement reconstruction restorate facial feature contours. This paper constructs a dataset of 2210 two-dimensional images with craniofacial depth information, which is used to train a CFRGAN model to realize facial reconstruction of skull images. Experiments are conducted from the perspectives of qualitative analysis and quantitative analysis. The results show that CFRGAN generated image retains more identity information, and the similarity between the reconstructed face image and the real face image reaches 94%, which is better than the existing methods. In summary, CFR-GAN proposed in this paper has ability to generate high-fidelity images and is efficient at craniofacial reconstructing.