Craniofacial Reconstruction Method Based on Region Fusion Strategy

Abstract

Craniofacial reconstruction is to estimate a person's face model from the skull. It can be applied in many fields such as forensic medicine, archaeology, and face animation. Craniofacial reconstruction is based on the relationship between the skull and the face to reconstruct the facial appearance from the skull. However, the craniofacial structure is very complex and the relationship is not the same in different craniofacial regions. To better represent the shape changes of the skull and face and make better use of the correlation between different local regions, a new craniofacial reconstruction method based on region fusion strategy is proposed in this paper. This method has the flexibility of finding the nonlinear relationship between skull and face variables and is easy to solve. Firstly, the skull and face are divided into five corresponding local regions; secondly, the five regions of skull and face are mapped to low-dimensional latent space using Gaussian process latent variable model (GP-LVM), and the nonlinear features between skull and face are extracted; then, least square support vector regression (LSSVR) model is trained in latent space to establish the mapping relationship between skull region and face region; finally, perform regional fusion to achieve overall reconstruction. For the unknown skull, first divide the region, then project it into the latent space of the skull region, then use the trained LSSVR model to reconstruct the face of the corresponding region, and finally perform regional fusion to realize the face reconstruction of the unknown skull. The experimental results show that the method is effective. Compared with other regression methods, our method is optimal. In addition, we add attributes such as age and body mass index (BMI) to the mappings to achieve face reconstruction with different attributes.

Figure 1

The skull and face of a sample in the uniform coordinate system.

Figure 2

The results of skull and face registration.

Figure 3

The framework of our method.

Figure 4

Results of various regions of the skull and face.

Figure 5

Schematic diagram of the grid interpolation process.

Figure 6

Regional fusion process.

Figure 7

Results of facial reconstruction of some samples.

Figure 8

Global-based reconstruction method.

Figure 9

Local-based reconstruction method (our method).

Figure 10

The average reconstruction error of the local and global craniofacial reconstruction method on 20 test samples was analyzed.

Figure 11

Comparison of the results of the three reconstruction methods.

Figure 12

Average reconstruction error of different methods on the test sample set.

Figure 13

The reconstruction results with variation of attributes.