NDLI: Edge-guided single facial depth map super-resolution using CNN

Content Provider	IET Digital Library
Author	Zhang, Fan Liu, Na Chang, Liang Duan, Fuqing Deng, Xiaoming
Abstract	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.
Starting Page	4708
Ending Page	4716
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 17, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/17
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1623
Journal	IET Image Processing
Publisher Date	2021-01-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	3D-face-related Studies Camera Computer Vision And Image Processing Technique Consumer Depth Cameras Convolutional Neural Nets Depth Reconstruction Sub-network Digital Entertainment Edge Detection Edge Guidance Map Edge Prediction Sub-network Edge-guided Convolutional Neural Network Edge-guided Single Facial Depth Map Super-resolution Face Poses Face Recognition Feature Extraction Human Face Human-machine Interaction Image Colour Analysis Image Recognition Image Reconstruction Image Resolution Image Sonsor Input Depth Maps Kinect V2 Facial Depth Data Rich 3D Information Sharp Edge Stereo Image Processing Super-resolved Depth Map Synthetic Facial Depth Data
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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