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Applied Sciences
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Deep Learning for Facial Expression Analysis
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Deep Learning for Facial Expression Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 2700

Special Issue Editors

Prof. Dr. Xianye Ben

E-Mail Website
Guest Editor
School of Information Science and Engineering, Shandong University, Qingdao 266237, China
Interests: image processing; pattern recognition; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tao Lei

E-Mail Website
Assistant Guest Editor
School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, China
Interests: image processing; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Chen

E-Mail Website
Assistant Guest Editor
Associate Professor, School of Information Science and Engineering, Shandong University, Qingdao, China
Interests: image processing and computer vision; machine learning and artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Zhang

E-Mail Website
Assistant Guest Editor
Associate Professor, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
Interests: video and image understanding; machine learning and deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue on Deep Learning for Facial Expression Analysis.

Facial expression analysis has been used in different applications to facilitate human–computer interaction. It deals with the problems of lighting, head pose, occlusion (such as from clothing, glasses, facial hair), subject race, subject identity, etc. Meanwhile, recent advances in deep learning have helped to solve many challenges in various fields, including facial expression analysis, computer vision, image processing, and natural language processing. There have been a number of developments demonstrating the feasibility of automated facial expression analysis systems for medical diagnose, automotive industries, education and entertainment.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the field of Deep Learning for Facial Expression Analysis. Researchers are welcome to submit research, technical, review, survey, or vision articles which contribute to algorithmic development, implementations, or real applications of facial expression analysis.

Prof. Dr. Xianye Ben
Prof. Dr. Tao Lei
Dr. Lei Chen
Dr. Peng Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • facial expression analysis
  • deep learning
  • deep neural networks
  • micro-expression recognition
  • micro-expression detection
  • facial action unit detection

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Published Papers (1 paper)

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Research

15 pages, 1584 KiB  
Article
AU-Guided Unsupervised Domain-Adaptive Facial Expression Recognition
by Xiaojiang Peng, Yuxin Gu and Panpan Zhang
Appl. Sci. 2022, 12(9), 4366; https://doi.org/10.3390/app12094366 - 26 Apr 2022
Cited by 5 | Viewed by 2035
Abstract
Domain diversities, including inconsistent annotation and varied image collection conditions, inevitably exist among different facial expression recognition (FER) datasets, posing an evident challenge for adapting FER models trained on one dataset to another one. Recent works mainly focus on domain-invariant deep feature learning [...] Read more.
Domain diversities, including inconsistent annotation and varied image collection conditions, inevitably exist among different facial expression recognition (FER) datasets, posing an evident challenge for adapting FER models trained on one dataset to another one. Recent works mainly focus on domain-invariant deep feature learning with adversarial learning mechanisms, ignoring the sibling facial action unit (AU) detection task, which has obtained great progress. Considering that AUs objectively determine facial expressions, this paper proposes an AU-guided unsupervised domain-adaptive FER (AdaFER) framework to relieve the annotation bias between different FER datasets. In AdaFER, we first leverage an advanced model for AU detection on both a source and a target domain. Then, we compare the AU results to perform AU-guided annotating, i.e., target faces that own the same AUs as source faces would inherit the labels from the source domain. Meanwhile, to achieve domain-invariant compact features, we utilize an AU-guided triplet training, which randomly collects anchor–positive–negative triplets on both domains with AUs. We conduct extensive experiments on several popular benchmarks and show that AdaFER achieves state-of-the-art results on all these benchmarks. Full article
(This article belongs to the Special Issue Deep Learning for Facial Expression Analysis)
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