NDLI: ApprGAN: appearance-based GAN for facial expression synthesis

Content Provider	IET Digital Library
Author	Peng, Yao Yin, Hujun
Abstract	Facial expression synthesis has drawn increasing attention in computer vision, graphics and animation. Recently, generative adversarial nets (GANs) have become a new perspective for face synthesis and have had remarkable success in generating photorealistic images and image-to-image translation. In this study, the authors present an appearance-based facial expression synthesis framework, ApprGAN, by combining shape and texture and introducing cycle consistency and identity mapping into the adversarial learning. Specifically, given an input face image, a pair of shape and texture generators are trained for synthetic shape deformation and expression detail generation, respectively. Extensive experiments on expression synthesis and cross-database synthesis were conducted, together with comparisons with the existing methods. Results of expression synthesis and quantitative verification on various databases show the effectiveness of ApprGAN in synthesising photorealistic and identity-preserving expressions and its marked improvement over the existing methods.
Starting Page	2706
Ending Page	2715
Page Count	10
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 14, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6576
Journal	IET Image Processing
Publisher	The Institution of Engineering and Technology
Publisher Date	2019-03-29
Access Restriction	Open
Rights License	Creative Commons Attribution -Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Subject Keyword	Adversarial Learning Animation Appearance-based Facial Expression Synthesis Framework Appearance-based GAN ApprGAN Computer Animation Computer Vision Computer Vision And Image Processing Technique Cross-database Synthesis Cycle-consistency Expression Detail Generation Face Recognition Face Synthesis Synthesis Generative Adversarial Nets Graphics Technique Identity Mapping Image Recognition Image Texture Image-to-image Translation Input Face Image Learning in AI Neural Computing Technique Neural Nets Photorealistic Identity-preserving Expressions Photorealistic Image Generation Quantitative Verification Synthetic Shape Deformation Texture Generator
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Interactive facial animation with deep neural networks

Influence of shape and texture features on facial expression recognition

Facial aging simulation via tensor completion and metric learning

Spatiotemporal feature extraction for facial expression recognition

Creative and diverse artwork generation using adversarial networks

Facial expression recognition considering individual differences in facial structure and texture

Using feature points and angles between them to recognise facial expression by a neural network approach

Face detection with a Viola–Jones based hybrid network

Image unmosaicing without location information using stacked GAN

ApprGAN: appearance-based GAN for facial expression synthesis

Similar Documents

Interactive facial animation with deep neural networks

Influence of shape and texture features on facial expression recognition

Facial aging simulation via tensor completion and metric learning

Spatiotemporal feature extraction for facial expression recognition

Creative and diverse artwork generation using adversarial networks

Facial expression recognition considering individual differences in facial structure and texture

Using feature points and angles between them to recognise facial expression by a neural network approach

Face detection with a Viola–Jones based hybrid network

Image unmosaicing without location information using stacked GAN

ApprGAN: appearance-based GAN for facial expression synthesis