基于改进二次误差测度及网格参数化的三维人脸本征属性描述方法

An Improved Quadric Error Metrics and Mesh Parameterization Based 3D Face Intrinsic Attributes Descriptor

3维人脸特征描述是3维人脸配准及识别的关键技术。该文针对3维人脸高分辨率模型特征分布不均匀且存在信息冗余的问题,提出一种基于模型简化和网格参数化的3维人脸特征描述方法。采用半边折叠及自适应收缩代价加权等手段对基于二次误差测度的网格简化方法进行改进,克服原算法中存在重叠三角形和丢失细节特征的问题。同时,基于多分辨分析思想,利用特征约束的保形同构映射对简化后的3维人脸模型在2维平面进行保形展开,并由此构造多分辨2维本征属性图。该方法将3维空间运算问题简化为简单的2维图像运算,显著降低了计算复杂度。对GavabDB 3维人脸库的识别实验表明,该文方法能有效描述3维人脸的本征属性,同时对数据缺失具有较强的鲁棒性。

A novel 3D face intrinsic attributes descriptor is proposed in this paper.The presented descriptor is used to solve the feature inhomogeneous distribution and redundancy for high resolution 3D face model.In presented method,an improved Quadric Error Metrics（QEM） mesh decimation method is first developed based on semi-edge folding and adaptive cost weighting.Multi-resolution 2D intrinsic attributed image can be then obtained by homeomorphically mapping 3D decimated facial mesh into 2D plane with the highest attribute preserving based on feature restricted conformal isomorphic mapping.Consequently,3D surface matching issue can be simplified to a 2D image matching issue by comparing the resulting 2D intrinsic attributed images,which are stable and robust to occlusion and noise.Experimental results on GavabDB show that presented method has the ability to represent intrinsic information of 3D face and achieve significant improvements on recognition accuracy compared with baselines.