Craniofacial Reconstruction via Face Elevation Map Estimation Based on the Deep Convolution Neutral Network

Yining Hu,Yueli Pan,Lizhe Xie,Zheng Wang,Zhe Wang

doi:10.1155/2021/9987792

Abstract

In this study, to achieve the possibility of predicting face by skull automatically, we propose a craniofacial reconstruction method based on the end-to-end deep convolutional neural network. Three-dimensional volume data are obtained from 1447 head CT scans of Chinese people of different ages. The facial and skull surface data are projected onto two-dimensional space to generate a two-dimensional elevation map, and then, use the deep convolution neural network to realize the prediction of skull to face shape in two-dimensional space. The encoder and decoder are composed of first feature extraction through the encoder and then as the input of the decoder to generate the craniofacial restoration image. In order to accurately describe the features of different scales, we adopt an U-shaped codec structure with cross-layer connections. Therefore, the output features are decomposed with the features of the corresponding scales in the encoding stage to achieve the integration of different scales while restoring the feature scales in the compression and decoding stage. Meanwhile, the U-net structures help to avoid the problem of loss of detail features in the downsampling process. We use supervised learning to obtain the prediction model from skull to facial elevation map. Back-projection operation is performed afterwards to generate facial surface data in 3D space. Experiments show that the proposed method in this study can effectively achieve craniofacial reconstruction, and for most part of the face, restoration error is controlled within 2 mm.

Highlights

Craniofacial reconstruction is a technique producing a reconstructed face from a human skull
Based on the relationship between the skull and face in forensic medicine, anthropology, and anatomy, this technique has been widely used in criminal investigation and archaeology. e traditional craniofacial reconstruction is mainly implemented manually by experts, based on the anatomical law of the human head and face on the plaster model of the victim’s skull and according to the relationship between the soft tissue of the human head and face and the morphological characteristics of the face and skull. e facial appearance of the victim is gradually reproduced with adding rubber clay and other materials. is method usually requires a complicated process, high cost, and time-consuming
With the rapid development of deep learning technology, data generation based on the convolutional neural network shows significant advantages, among which the representative technologies are the variational autoencoder (VAE) [15, 16] and generative adversarial network (GAN) [17]

Summary

Introduction

Craniofacial reconstruction is a technique producing a reconstructed face from a human skull. With the rapid development of deep learning technology, data generation based on the convolutional neural network shows significant advantages, among which the representative technologies are the variational autoencoder (VAE) [15, 16] and generative adversarial network (GAN) [17]. The reconstruction accuracy is relatively high, the common problem of the template method is inevitable, that is, the generation process is cumbersome, and the network structure is complex. Based on the above research, we propose an end-to-end facial morphology prediction method based on the deep convolutional neural network to automatically estimate face information from skull data. For the proposed method, named cylindrical facial projection residual net (CFPRN), it needs neither preset face template nor feature point detection. We use U-shape network structure so as to adapt with features of different scales. e CFPRN is easy to implement, and experiments have verified the robustness and accuracy of the proposed method

Data Preprocessing

Experiment

Results and Discussion

Conclusion and Prospects