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Advanced Technologies in Gait Recognition
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Advanced Technologies in Gait Recognition

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 (20 March 2024) | Viewed by 4395

Special Issue Editors

Dr. Tee Connie

E-Mail Website
Guest Editor
Associate Professor, Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
Interests: computer vision; machine learning; digital image processing; biometrics
Prof. Dr. Ong Thian Song

E-Mail Website
Guest Editor
Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
Interests: biometric security; machine learning
Dr. Md. Shohel Sayeed

E-Mail Website
Guest Editor
Associate Professor, Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
Interests: biometrics; machine learning and deep learning; image and signal processing; soft computing and data mining

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to recent advancements in gait recognition, which has become an important indicator for remote biometric identification where a person can be identified at a distance without direct contact. By studying the walking movement of a person, we can also determine normal gait behaviors and diagnose potential issues related to gait abnormalities. For example, evidence has shown there to be correlations between Parkinson’s disease and gait patterns. There are also other emerging applications of gait analysis, such as gender and age estimation using gait signatures.

In this Special Issue, we invite submissions exploring novel technological advances in gait recognition. We welcome novel contributions regarding design, analysis and algorithms for gait analysis in different fields. Potential topics include, but are not limited to, the following:

  • Gait recognition;
  • Machine learning and deep learning;
  • Computer vision;
  • Biometrics;
  • Healthcare;
  • Smart surveillance;
  • Wearable sensors;
  • Data-driven monitoring;
  • Big data.

Dr. Tee Connie
Prof. Dr. Ong Thian Song
Dr. Md. Shohel Sayeed
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. 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.

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Published Papers (3 papers)

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Research

16 pages, 1194 KiB  
Article
CoreTemp: Coreset Sampled Templates for Multimodal Mobile Biometrics
by Jaeho Yoon, Jaewoo Park, Jungyun Kim and Andrew Beng Jin Teoh
Appl. Sci. 2024, 14(12), 5183; https://doi.org/10.3390/app14125183 - 14 Jun 2024
Viewed by 1405
Abstract
Smart devices have become the core ingredient in maintaining human society, where their applications span basic telecommunication, entertainment, education, and even critical security tasks. However, smartphone security measures have not kept pace with their ubiquitousness and convenience, exposing users to potential security breaches. [...] Read more.
Smart devices have become the core ingredient in maintaining human society, where their applications span basic telecommunication, entertainment, education, and even critical security tasks. However, smartphone security measures have not kept pace with their ubiquitousness and convenience, exposing users to potential security breaches. Shading light on shortcomings of traditional security measures such as PINs gives rise to biometrics-based security measures. Open-set authentication with pretrained Transformers especially shows competitive performance in this context. Bringing this closer to practice, we propose CoreTemp, a greedy coreset sampled template, which offers substantially faster authentication speeds. In parallel with CoreTemp, we design a fast match algorithm where the combination shows robust performance in open-set mobile biometrics authentication. Designed to resemble the effects of ensembles with marginal increment in computation, we propose PIEformer+, where its application with CoreTemp has state-of-the-art performance. Benefiting from much more efficient authentication speeds to the best of our knowledge, we are the first to attempt identification in this context. Our proposed methodology achieves state-of-the-art results on HMOG and BBMAS datasets, particularly with much lower computational costs. In summary, this research introduces a novel integration of greedy coreset sampling with an advanced form of pretrained, implicitly ensembled Transformers (PIEformer+), greatly enhancing the speed and efficiency of mobile biometrics authentication, and also enabling identification, which sets a new benchmark in the relevant field. Full article
(This article belongs to the Special Issue Advanced Technologies in Gait Recognition)
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16 pages, 2264 KiB  
Article
Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
by Diyuan Guan, Chunsheng Hua and Xiaoheng Zhao
Appl. Sci. 2023, 13(23), 12808; https://doi.org/10.3390/app132312808 - 29 Nov 2023
Cited by 2 | Viewed by 1097
Abstract
Gait recognition is a distinctive biometric technique that can identify pedestrians by their walking patterns from considerable distances. A critical challenge in gait recognition lies in effectively acquiring discriminative spatial-temporal representations from silhouettes that exhibit invariance to disturbances. In this paper, we present [...] Read more.
Gait recognition is a distinctive biometric technique that can identify pedestrians by their walking patterns from considerable distances. A critical challenge in gait recognition lies in effectively acquiring discriminative spatial-temporal representations from silhouettes that exhibit invariance to disturbances. In this paper, we present a novel gait recognition network by aggregating features in the spatial-temporal and view domains, which consists of two-path spatial-temporal feature fusion module and view embedding module. Specifically, two-path spatial-temporal feature fusion module firstly utilizes multi-scale feature extraction (MSFE) to enrich the input features with multiple convolution kernels of various sizes. Then, frame-level spatial feature extraction (FLSFE) and multi-scale temporal feature extraction (MSTFE) are parallelly constructed to capture spatial and temporal gait features of different granularities and these features are fused together to obtain muti-scale spatial-temporal features. FLSFE is designed to extract both global and local gait features by employing a specially designed residual operation. Simultaneously, MSTFE is applied to adaptively interact multi-scale temporal features and produce suitable motion representations in temporal domain. Taking into account the view information, we introduce a view embedding module to reduce the impact of differing viewpoints. Through the extensive experimentation over CASIA-B and OU-MVLP datasets, the proposed method has achieved superior performance to the other state-of-the-art gait recognition approaches. Full article
(This article belongs to the Special Issue Advanced Technologies in Gait Recognition)
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11 pages, 2003 KiB  
Article
Gait Recognition Based on Gait Optical Flow Network with Inherent Feature Pyramid
by Hongyi Ye, Tanfeng Sun and Ke Xu
Appl. Sci. 2023, 13(19), 10975; https://doi.org/10.3390/app131910975 - 5 Oct 2023
Cited by 2 | Viewed by 1349
Abstract
Gait is a kind of biological behavioral characteristic which can be recognized from a distance and has gained an increased interest nowadays. Many existing silhouette-based methods ignore the instantaneous motion of gait, which is an important factor in distinguishing people with similar shapes. [...] Read more.
Gait is a kind of biological behavioral characteristic which can be recognized from a distance and has gained an increased interest nowadays. Many existing silhouette-based methods ignore the instantaneous motion of gait, which is an important factor in distinguishing people with similar shapes. To further emphasize the instantaneous motion factor in human gait, the Gait Optical Flow Image (GOFI) is proposed to add the instantaneous motion direction and intensity to original gait silhouettes. The GOFI also helps to leverage both the temporal and spatial condition noises. Then, the gait features are extracted by the Gait Optical Flow Network (GOFN), which contains a Set Transition (ST) architecture to aggregate the image-level features to the set-level features and an Inherent Feature Pyramid (IFP) to exploit the multi-scaled partial features. The combined loss function is used to evaluate the similarity between different gaits. Experiments are conducted on two widely used gait datasets, the CASIA-B and the CASIA-C. The experiments show that the GOFN performs better on both datasets, which shows the effectiveness of the GOFN. Full article
(This article belongs to the Special Issue Advanced Technologies in Gait Recognition)
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