Special Issue "3D Human Understanding"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Biometrics, Forensics, and Security".

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Dr. Claudio Ferrari
E-Mail Website
Guest Editor
Media Integration and Communication Center, University of Florence, Florence 50139, Italy
Interests: biometrics; 3D face modeling and reconstruction; deep learning
Prof. Dr. Stefano Berretti
E-Mail Website
Guest Editor
Department of Information Engineering (DINFO), University of Firenze, via S. Marta 3, 50139 Firenze, Italy
Interests: 3D computer vision; pattern recognition; 3D/4D biometrics
Dr. Giuseppe Lisanti
E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Bologna, Mura Anteo Zamboni 7, Bologna 40126, Italy
Interests: face and expression recognition; person re-identification; deep learning
Prof. Dr. Liming Chen
E-Mail Website
Guest Editor
Department of Mathematics and Informatics, Ecole Centrale de Lyon, 36, avenue Guy de Collongue 69131 ECULLY Cedex, France
Interests: face analysis and recognition; image classification; transfer learning; deep learning

Special Issue Information

Dear Colleagues,

The significant recent advancements in research fields such as robotics, autonomous driving, or human–machine interaction have strongly renewed the interest in developing automatic systems capable of interacting with the 3D world. To this aim, interpreting the behavior of humans represents a crucial step toward the development of systems able to naturally blend into the real world. 3D data represent a richer source of information compared to 2D images or video sequences, and the development of new affordable and accurate 3D acquisition sensors is making the application of algorithms possible in real scenarios, further posing new challenges and practical issues.

In detail, this Special Issue aims to collect a diverse and complementary set of articles proposing new and competitive theories and applications of 3D vision applied to the analysis of humans.

Dr. Claudio Ferrari
Prof. Dr. Stefano Berretti
Dr. Giuseppe Lisanti
Prof. Dr. Liming Chen
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

  • 3D biometrics
  • 3D action recognition
  • 3D head/body reconstruction and modeling
  • body pose
  • gesture recognition

Published Papers (1 paper)

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Research

Article
Learning Descriptors Invariance through Equivalence Relations within Manifold: A New Approach to Expression Invariant 3D Face Recognition
J. Imaging 2020, 6(11), 112; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6110112 - 22 Oct 2020
Viewed by 507
Abstract
This paper presents a unique approach for the dichotomy between useful and adverse variations of key-point descriptors, namely the identity and the expression variations in the descriptor (feature) space. The descriptors variations are learned from training examples. Based on labels of the training [...] Read more.
This paper presents a unique approach for the dichotomy between useful and adverse variations of key-point descriptors, namely the identity and the expression variations in the descriptor (feature) space. The descriptors variations are learned from training examples. Based on labels of the training data, the equivalence relations among the descriptors are established. Both types of descriptor variations are represented by a graph embedded in the descriptor manifold. Invariant recognition is then conducted as a graph search problem. A heuristic graph search algorithm suitable for the recognition under this setup was devised. The proposed approach was tested on the FRGC v2.0, the Bosphorus and the 3D TEC datasets. It has shown to enhance the recognition performance, under expression variations, by considerable margins. Full article
(This article belongs to the Special Issue 3D Human Understanding)
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