提出并实现一种基于两张正交图像和一个标准3维头模型，并利用2D图像特征点和3D模型特征点的匹配进行3维头模型重建的算法。首先，进行面部区域和头发区域的分割，利用色彩传递对输入图像进行颜色处理。对正面图像利用改进后的ASM（主动形状模型）模型进行特征点定位。改进局部最大曲率跟踪(LMCT)方法，更为鲁棒的定位了侧面特征点。在匹配图像特征点与标准3维头上预先定义的特征点的基础上，利用径向基函数进行标准头形变，获得特定人的3维头部形状模型。采用重建好的3维头作为桥梁，自动匹配输入图像，进行无缝纹理融合。最后，将所得纹理映射到形状模型上，获得对应输入图像的特定真实感3维头模型。

We proposes a framework for reconstructing one 3D head model based on two orthogonal images and one 3D generic head model through feature matching. First, we segment the images into face and hair regions. For frontal images, feature points are located by an improved ASM (active shape model), for side images, feature points are found by an improved local maximum curvature tracking method. In this method, we select some distinctive feature points from the generic 3D model. These 3D feature points are matched to the detected image feature points. Based on the radical basis function (RBF), the generic 3D model is morphed, and the shape model is obtained. Since the images from orthogonal views may have different illumination, color transfer is used to enforce the texture color consistency. Based on the shape model, these two images are matched, and one seamless texture is generated. The final head reconstruction is achieved by mapping the texture to the 3D head model.