Edge‐guided single facial depth map super‐resolution using CNN
Fan Zhang, Na Liu, Liang Chang, Fuqing Duan, Xiaoming Deng · IET Image Processing · 2020
In recent years, consumer depth cameras have been widely used in digital entertainment and human‐machine interaction due to the advantages of real‐time performance and low cost. Facial depth maps have shown great potential in 3D‐face‐related studies. However, disadvantages of low resolution and precision limit its further applications. In this work, the authors propose an edge‐guided convolutional neural network for single facial depth map super‐resolution. It consists of two parts: an edge prediction sub‐network and a depth reconstruction sub‐network. The edge prediction sub‐network generates an edge guidance map to guide the depth reconstruction sub‐network to recover sharp edges and fine structures. Effective data augmentation methods are proposed as well. The network is patch‐based and able to cope with any size of the input depth maps. In addition, it is insensitive to the face pose since the synthetic training dataset they generated covers a wide range of face poses. The proposed method is validated with three datasets including a synthetic facial depth data set, a real Kinect V2 facial depth data set and Middlebury Stereo Data set. Experimental results show that it outperforms the state‐of‐the‐art methods on all the three data sets.