College of Information Science and Technology, Northwest University, Xi'an, China.
Biomed Res Int. 2020 Dec 4;2020:8835179. doi: 10.1155/2020/8835179. eCollection 2020.
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.
颅面重建是从颅骨估计一个人的面部模型。它可以应用于法医、考古和面部动画等许多领域。颅面重建是基于颅骨和面部之间的关系，从颅骨重建面部外观。然而，颅面结构非常复杂，不同颅面区域之间的关系也不相同。为了更好地表示颅骨和面部的形状变化，并更好地利用不同局部区域之间的相关性，本文提出了一种基于区域融合策略的新颅面重建方法。该方法具有发现颅骨和面部变量之间非线性关系的灵活性，易于求解。首先，将颅骨和面部划分为五个相应的局部区域；其次，使用高斯过程潜在变量模型 (GP-LVM) 将颅骨和面部的五个区域映射到低维潜在空间，并提取颅骨和面部之间的非线性特征；然后，在潜在空间中训练最小二乘支持向量回归 (LSSVR) 模型，建立颅骨区域和面部区域之间的映射关系；最后，进行区域融合，实现整体重建。对于未知颅骨，首先进行区域划分，然后将其投影到颅骨区域的潜在空间中，再使用训练好的 LSSVR 模型对相应区域的面部进行重建，最后进行区域融合，实现未知颅骨的面部重建。实验结果表明，该方法是有效的。与其他回归方法相比，我们的方法是最优的。此外，我们还将年龄和体重指数 (BMI) 等属性添加到映射中，以实现具有不同属性的面部重建。
