标题：The Depth Estimation of 3D Face from Single 2D Picture based on Manifold Learning Constraints
作者：Li, Xia; Yang, Yang; Xiong, Hailiang; Liu, Yunxia
作者机构：[Li, Xia; Yang, Yang; Xiong, Hailiang] Shandong Univ Jinan, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China.; [Liu, Yunxia] Ji Nan Univ, S 更多
会议名称：9th International Conference on Graphic and Image Processing (ICGIP)
会议日期：OCT 14-16, 2017
来源：NINTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2017)
关键词：t-SNE; Manifold Learning; K-means; Depth Estimation; Delaunay; triangulation
摘要：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 1x249 to 1x2. 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%.