三维人脸建模及在跨姿态人脸匹配中的有效性验证

针对现有三维人脸采集技术对采集场景存在诸多限制，提出了自由场景下基于多张图像的三维人脸建模技术，并对其进行了有效性验证。首先，提出一个姿态及深度值迭代计算模型，实现了特征点深度值的准确估计；然后，进行了基于多张图像的深度值融合及整体形状建模；最后，将深度迭代优化算法（IPDO）与目前最优的非线性最小二乘法（NLS1_SR）在Bosphorus Database数据集上进行了对比，建模精度提高了9%，所重建的三维人脸模型投影图像与二维图像具有较高的相似度。实验结果表明，在大姿态变化条件下，该识别算法借助三维信息相较于未借助的情况下，其识别率可以提高50%以上。

3D face modeling and validation in cross-pose face matching

Since the existing 3D face acquisition technology has many restrictions on gathering scene, a 3D face reconstruction technology based on several images was proposed, and its validation was verified. First, an iterative computing model of pose and depth value estimation was proposed to implement the accurate estimation of feature depth. Then the depth values integration based on several images and shape modeling were further investigated. Finally, the Iterative Pose and Depth Optimization (IPDO) algorithm was compared with Nonlinear Least-Squares Model with Symmetry and Regularization terms (NLS1_SR) on Bosphorus database, the modeling precision was improved by 9%, and the projected image of 3D model is similar to the 2D inputted image. The experimental results show that under the condition of big pose change, the proposed recognition algorithm assisted by 3D information can improve the recognition rate of more than 50%.