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J. Imaging
Special Issues
3D Human Understanding
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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: closed (31 July 2021) | Viewed by 19671

Special Issue Editors

Dr. Claudio Ferrari

E-Mail Website
Guest Editor
Media Integration and Communication Center, University of Florence, 50139 Florence, Italy
Interests: biometrics; 3D face modeling and reconstruction; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Stefano Berretti

E-Mail Website
Guest Editor
Media Integration and Communication Center (MICC), Department of Information Engineering (DINFO), University of Firenze, Via S. Marta 3, 50139 Firenze, Italy
Interests: multimedia; 3D computer vision; articifial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Lisanti

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Bologna, Mura Anteo Zamboni 7, 40126 Bologna, 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 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. 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 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

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

Published Papers (5 papers)

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Research

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12 pages, 6238 KiB  
Article
Monocular 3D Body Shape Reconstruction under Clothing
by Claudio Ferrari, Leonardo Casini, Stefano Berretti and Alberto Del Bimbo
J. Imaging 2021, 7(12), 257; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7120257 - 30 Nov 2021
Cited by 3 | Viewed by 3159
Abstract
Estimating the 3D shape of objects from monocular images is a well-established and challenging task in the computer vision field. Further challenges arise when highly deformable objects, such as human faces or bodies, are considered. In this work, we address the problem of [...] Read more.
Estimating the 3D shape of objects from monocular images is a well-established and challenging task in the computer vision field. Further challenges arise when highly deformable objects, such as human faces or bodies, are considered. In this work, we address the problem of estimating the 3D shape of a human body from single images. In particular, we provide a solution to the problem of estimating the shape of the body when the subject is wearing clothes. This is a highly challenging scenario as loose clothes might hide the underlying body shape to a large extent. To this aim, we make use of a parametric 3D body model, the SMPL, whose parameters describe the body pose and shape of the body. Our main intuition is that the shape parameters associated with an individual should not change whether the subject is wearing clothes or not. To improve the shape estimation under clothing, we train a deep convolutional network to regress the shape parameters from a single image of a person. To increase the robustness to clothing, we build our training dataset by associating the shape parameters of a “minimally clothed” person to other samples of the same person wearing looser clothes. Experimental validation shows that our approach can more accurately estimate body shape parameters with respect to state-of-the-art approaches, even in the case of loose clothes. Full article
(This article belongs to the Special Issue 3D Human Understanding)
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25 pages, 26277 KiB  
Article
Robust 3D Face Reconstruction Using One/Two Facial Images
by Ola Lium, Yong Bin Kwon, Antonios Danelakis and Theoharis Theoharis
J. Imaging 2021, 7(9), 169; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7090169 - 30 Aug 2021
Cited by 2 | Viewed by 4144
Abstract
Being able to robustly reconstruct 3D faces from 2D images is a topic of pivotal importance for a variety of computer vision branches, such as face analysis and face recognition, whose applications are steadily growing. Unlike 2D facial images, 3D facial data are [...] Read more.
Being able to robustly reconstruct 3D faces from 2D images is a topic of pivotal importance for a variety of computer vision branches, such as face analysis and face recognition, whose applications are steadily growing. Unlike 2D facial images, 3D facial data are less affected by lighting conditions and pose. Recent advances in the computer vision field have enabled the use of convolutional neural networks (CNNs) for the production of 3D facial reconstructions from 2D facial images. This paper proposes a novel CNN-based method which targets 3D facial reconstruction from two facial images, one in front and one from the side, as are often available to law enforcement agencies (LEAs). The proposed CNN was trained on both synthetic and real facial data. We show that the proposed network was able to predict 3D faces in the MICC Florence dataset with greater accuracy than the current state-of-the-art. Moreover, a scheme for using the proposed network in cases where only one facial image is available is also presented. This is achieved by introducing an additional network whose task is to generate a rotated version of the original image, which in conjunction with the original facial image, make up the image pair used for reconstruction via the previous method. Full article
(This article belongs to the Special Issue 3D Human Understanding)
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21 pages, 4990 KiB  
Article
Efficient Face Recognition System for Operating in Unconstrained Environments
by Alejandra Sarahi Sanchez-Moreno, Jesus Olivares-Mercado, Aldo Hernandez-Suarez, Karina Toscano-Medina, Gabriel Sanchez-Perez and Gibran Benitez-Garcia
J. Imaging 2021, 7(9), 161; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7090161 - 26 Aug 2021
Cited by 19 | Viewed by 5743
Abstract
Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient [...] Read more.
Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together. Full article
(This article belongs to the Special Issue 3D Human Understanding)
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19 pages, 1229 KiB  
Article
Learning Descriptors Invariance through Equivalence Relations within Manifold: A New Approach to Expression Invariant 3D Face Recognition
by Faisal R. Al-Osaimi
J. Imaging 2020, 6(11), 112; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6110112 - 22 Oct 2020
Cited by 2 | Viewed by 1638
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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Review

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20 pages, 2675 KiB  
Review
Using Inertial Sensors to Determine Head Motion—A Review
by Severin Ionut-Cristian and Dobrea Dan-Marius
J. Imaging 2021, 7(12), 265; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7120265 - 06 Dec 2021
Cited by 12 | Viewed by 3976
Abstract
Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices [...] Read more.
Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases—IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body. Full article
(This article belongs to the Special Issue 3D Human Understanding)
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