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The depth map obtained by Microsoft Kinect is often accompanied with a large area information loss called black hole. In this paper, an optimal algorithm for image restoration with multi-resolution anisotropic diffusion is proposed to fill the black areas. At the same times, the restriction of anisotropic diffusion algorithm can be broken through by using multi-resolution. Using the color map as a guidance to refine the details of the image. Then according to local adjustment, the error rate can be effectively reduced. At the end of this paper, the joint bilateral filter (JBF) is introduced as a contrast, and the PCL open source point cloud database is used for 3D reconstruction. Compared with competing methods, the proposed algorithm can fill the larger black holes significantly. The experiments show that it also has good performance on reducing the error rate and preserving the edge details.

In 2D face recognition, result may suffer from the impact of varying pose, expression, and illumination conditions. However, 3D face recognition utilizes depth information to enhance systematic robustness. Thus, an improved deep convolutional neural network (DCNN) combined with softmax classifier to identify face is trained. First, the preprocessing of color image and depth map is different in removing redundant information. Then, the feature extraction networks for 2D face image and depth map are, respectively, build with the principle of recognition rate maximization, and parameters about neural networks reset by a series of tests, in order to acquire higher recognition rate. At last, the fusion of two feature layers is the final input of artificial neural network (ANN) recognition system, which is followed by a 64-way softmax output. Experimental results demonstrate that it is effective in improving recognition rate.