Special Issue "Imaging Studies for Face and Gesture Analysis"

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 1 September 2021.

Special Issue Editors

Dr. Moi Hoon Yap
E-Mail Website
Guest Editor
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
Interests: face informatics; micro-expression; human behavior; human motion; gesture
Special Issues and Collections in MDPI journals
Dr. Walied Ali Merghani
E-Mail Website
Guest Editor
Sudan University of Science and Technology, Khartoum, Sudan
Interests: facial micro-expressions; face recognition; face and gestures analysis; hand written recognition; OCR recognition
Dr. Remah Mutasim Ibrahim Albashir
E-Mail
Guest Editor
Department of Computer Science, Shaqra University, Riyadh, Saudi Arabia
Interests: digital image processing; facial wrinkle detection; facial features analysis; face age estimation
Dr. Omaima FathElrahman Osman
E-Mail Website
Guest Editor
School of Computer Science and Information Technology, Sharg El-Neil College, Khartoum, Sudan
Interests: face analysis; face age estimation; facial wrinkle detection

Special Issue Information

Dear Colleagues,

Face and (body) gestures are rapidly becoming an area of intense interest in computer science and human–computer interaction. Face and body gestures are considered as signals to enable better communication and the study of human behaviors. They are mostly used in security applications in public places such as banks and airports while also being used for marketing and entertainment. However, due to variations in face images and that people may pose the same gesture differently, efficient methods/algorithms for their analysis are still needed. This Special Issue focuses on imaging studies for face and gesture analysis, including the creation of new datasets, development of new technology/algorithms for measuring human behaviors, psychology/perception of human behaviors, image processing/machine learning and deep learning in face and gesture analysis, and the implication of human behaviors for healthcare applications. We aim to promote interactions between researchers, scholars, practitioners, engineers, and students from across industry and academia to discuss all aspects of human behaviors. We welcome original works that address a wide range of issues, including, but not limited to:

  • Face and gesture recognition;
  • Face age estimation and facial wrinkle analysis;
  • Face inpainting;
  • Subtle/microface and gesture movement analysis;
  • Technology in automated human behavior measurement;
  • Machine learning in human behavior analysis;
  • Real-time face and motion analysis;
  • Face and motion recognition on mobile devices;
  • Analysis of human motion (face and/or body gesture) for healthcare applications;
  • Novel datasets for face and gesture analysis;
  • Applications in face and gesture analysis;

Applications in other domains are welcome, though we ask that you please contact the Guest Editors.

Dr. Moi Hoon Yap
Dr. Walied Ali Merghani
Dr. Remah Mutasim Ibrahim Albashir
Dr. Omaima FathElrahman Osman
Guest Editors

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 papers will be 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. Journal of Imaging is an international peer-reviewed open access monthly 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 1600 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

  • face recognition
  • face age estimation
  • facial microexpressions
  • face informatics
  • human motion analysis
  • face and gestures analysis
  • behavioral analysis
  • facial wrinkle detection
  • face inpainting

Published Papers (1 paper)

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Research

Article
FACS-Based Graph Features for Real-Time Micro-Expression Recognition
J. Imaging 2020, 6(12), 130; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120130 - 30 Nov 2020
Viewed by 736
Abstract
Several studies on micro-expression recognition have contributed mainly to accuracy improvement. However, the computational complexity receives lesser attention comparatively and therefore increases the cost of micro-expression recognition for real-time application. In addition, majority of the existing approaches required at least two frames (i.e., [...] Read more.
Several studies on micro-expression recognition have contributed mainly to accuracy improvement. However, the computational complexity receives lesser attention comparatively and therefore increases the cost of micro-expression recognition for real-time application. In addition, majority of the existing approaches required at least two frames (i.e., onset and apex frames) to compute features of every sample. This paper puts forward new facial graph features based on 68-point landmarks using Facial Action Coding System (FACS). The proposed feature extraction technique (FACS-based graph features) utilizes facial landmark points to compute graph for different Action Units (AUs), where the measured distance and gradient of every segment within an AU graph is presented as feature. Moreover, the proposed technique processes ME recognition based on single input frame sample. Results indicate that the proposed FACS-baed graph features achieve up to 87.33% of recognition accuracy with F1-score of 0.87 using leave one subject out cross-validation on SAMM datasets. Besides, the proposed technique computes features at the speed of 2 ms per sample on Xeon Processor E5-2650 machine. Full article
(This article belongs to the Special Issue Imaging Studies for Face and Gesture Analysis)
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