Special Issue "Human Behavioral Analysis for Face and Gesture: Pathways to automation"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer and Engineering Science and Symmetry/Asymmetry".

Deadline for manuscript submissions: closed (31 August 2019).

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. Daniel Leightley
E-Mail Website
Guest Editor
King’s College London, United Kingdom
Interests: mobile health; machine learning; big data and human behavior analysis
Dr. Ryan Cunningham
E-Mail Website
Guest Editor
Manchester Metropolitan University, United Kingdom
Interests: machine learning; image processing; pattern recognition; deep learning and computer vision

Special Issue Information

Dear Colleagues,

Human face and body are symmetrical, research to understand symmetry and asymmetry is important for human behavioral analysis, particularly for automated analysis to provide objective measurements, assessments and evaluations. Recent advances in imaging and sensing have enabled rapid growth in the field of human behavioral analysis. Human behaviour is a revealing trait and a vital biometric characteristic. It is able to provide insight into a person’s health, age, emotions and feelings, mental health and intentions. In addition, new modalities of data (i.e., passive data collection from mobile, wearable technology) provide additional ‘context’ to what is observed through sensing. Combining these technologies has led to an increased understanding of human behavior, particularly in the field of face and gesture analysis for use in healthcare, gaming, and retail.

This Special Issue focuses on datasets/data sharing, technological challenges in the analysis/measurement of human behaviors, the implication of human behaviors for clinical/healthcare applications and human perception/psychology/physiology on human behaviors. We aim to promote interactions between researchers, scholars, practitioners, engineers and students, from across industry and academia, on all aspects of human behavior. Cross-discipline work is highly encouraged. We welcome original works that address a wide range of issues, including, but not limited to: 

  • Subtle/micro face and gesture movements analysis;
  • Technology in automated human behavior measurement;
  • Machine learning and deep learning in human behavior analysis;
  • Real-time face and motion analysis;
  • Face and motion recognition on mobile devices;
  • Analysis of human motion for clinical application;
  • Face analysis for clinical application;
  • The cost advantages of behavior technology in-the-wild;
  • Uniting sensing and wearable technology;
  • Novel datasets for human behavioral analysis;
  • The study/pilot of symmetrical analysis on face and gesture and its implications;

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

Dr. Moi Hoon Yap
Dr. Daniel Leightley
Dr. Ryan Cunningham
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. Symmetry 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 1800 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 informatics
  • facial micro-expressions
  • human behavior analysis
  • human motion
  • gesture

Published Papers (2 papers)

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Research

Article
An Improved Micro-Expression Recognition Method Based on Necessary Morphological Patches
Symmetry 2019, 11(4), 497; https://doi.org/10.3390/sym11040497 - 05 Apr 2019
Cited by 8 | Viewed by 954
Abstract
Micro-expression is a spontaneous emotional representation that is not controlled by logic. A micro-expression is both transitory (short duration) and subtle (small intensity), so it is difficult to detect in people. Micro-expression detection is widely used in the fields of psychological analysis, criminal [...] Read more.
Micro-expression is a spontaneous emotional representation that is not controlled by logic. A micro-expression is both transitory (short duration) and subtle (small intensity), so it is difficult to detect in people. Micro-expression detection is widely used in the fields of psychological analysis, criminal justice and human-computer interaction. Additionally, like traditional facial expressions, micro-expressions also have local muscle movement. Psychologists have shown micro-expressions have necessary morphological patches (NMPs), which are triggered by emotion. Furthermore, the objective of this paper is to sort and filter these NMPs and extract features from NMPs to train classifiers to recognize micro-expressions. Firstly, we use the optical flow method to compare the on-set frame and the apex frame of the micro-expression sequences. By doing this, we could find facial active patches. Secondly, to find the NMPs of micro-expressions, this study calculates the local binary pattern from three orthogonal planes (LBP-TOP) operators and cascades them with optical flow histograms to form the fusion features of the active patches. Finally, a random forest feature selection (RFFS) algorithm is used to identify the NMPs and to characterize them via support vector machine (SVM) classifier. We evaluated the proposed method on two popular publicly available databases: CASME II and SMIC. Results show that NMPs are statistically determined and contribute to significant discriminant ability instead of holistic utilization of all facial regions. Full article
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Article
Hyperspectral Face Recognition with Patch-Based Low Rank Tensor Decomposition and PFFT Algorithm
Symmetry 2018, 10(12), 714; https://doi.org/10.3390/sym10120714 - 04 Dec 2018
Cited by 1 | Viewed by 1046
Abstract
Hyperspectral imaging technology with sufficiently discriminative spectral and spatial information brings new opportunities for robust facial image recognition. However, hyperspectral imaging poses several challenges including a low signal-to-noise ratio (SNR), intra-person misalignment of wavelength bands, and a high data dimensionality. Many studies have [...] Read more.
Hyperspectral imaging technology with sufficiently discriminative spectral and spatial information brings new opportunities for robust facial image recognition. However, hyperspectral imaging poses several challenges including a low signal-to-noise ratio (SNR), intra-person misalignment of wavelength bands, and a high data dimensionality. Many studies have proven that both global and local facial features play an important role in face recognition. This research proposed a novel local features extraction algorithm for hyperspectral facial images using local patch based low-rank tensor decomposition that also preserves the neighborhood relationship and spectral dimension information. Additionally, global contour features were extracted using the polar discrete fast Fourier transform (PFFT) algorithm, which addresses many challenges relevant to human face recognition such as illumination, expression, asymmetrical (orientation), and aging changes. Furthermore, an ensemble classifier was developed by combining the obtained local and global features. The proposed method was evaluated by using the Poly-U Database and was compared with other existing hyperspectral face recognition algorithms. The illustrative numerical results demonstrate that the proposed algorithm is competitive with the best CRC_RLS and PLS methods. Full article
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