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首页> 外文会议>International conference on graphic and image processing >The Depth Estimation of 3D Face from Single 2D Picture based on Manifold Learning Constraints
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The Depth Estimation of 3D Face from Single 2D Picture based on Manifold Learning Constraints

机译：基于流形学习约束的单2D图片3D人脸深度估计

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The estimation of depth is virtual important in 3D face reconstruction. In this paper, we propose a t-SNE based on manifold learning constraints and introduce K-means method to divide the original database into several subset, and the selected optimal subset to reconstruct the 3D face depth information can greatly reduce the computational complexity. Firstly, we carry out the t-SNE operation to reduce the key feature points in each 3D face model from 1×249 to 1×2. Secondly, the K-means method is applied to divide the training 3D database into several subset. Thirdly, the Euclidean distance between the 83 feature points of the image to be estimated and the feature point information before the dimension reduction of each cluster center is calculated. The category of the image to be estimated is judged according to the minimum Euclidean distance. Finally, the method Kong D will be applied only in the optimal subset to estimate the depth value information of 83 feature points of 2D face images. Achieving the final depth estimation results, thus the computational complexity is greatly reduced. Compared with the traditional traversal search estimation method, although the proposed method error rate is reduced by 0.49, the number of searches decreases with the change of the category. In order to validate our approach, we use a public database to mimic the task of estimating the depth of face images from 2D images. The average number of searches decreased by 83.19%.
机译：深度估计在3D人脸重建中非常重要。在本文中，我们提出了一种基于流形学习约束的t-SNE，并引入了K-means方法将原始数据库划分为多个子集，而选择的最优子集来重构3D人脸深度信息可以大大降低计算复杂度。首先，我们执行t-SNE操作以将每个3D人脸模型中的关键特征点从1×249减少到1×2。其次，采用K-means方法将训练3D数据库划分为几个子集。第三，计算待估计图像的83个特征点与每个聚类中心的降维之前的特征点信息之间的欧几里得距离。根据最小欧几里得距离来判断要估计图像的类别。最终，仅在最优子集中应用方法Kong D来估计2D面部图像的83个特征点的深度值信息。获得最终的深度估计结果，因此大大降低了计算复杂度。与传统的遍历搜索估计方法相比，尽管提出的方法错误率降低了0.49，但搜索次数却随着类别的变化而减少。为了验证我们的方法，我们使用一个公共数据库来模拟从2D图像估计面部图像深度的任务。平均搜索量下降了83.19％。

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