Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.4
4.0
Journals
Sensors
Special Issues
Smart Sensors and Imaging for Face and Gesture Recognition
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Smart Sensors and Imaging for Face and Gesture Recognition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 20 October 2026 | Viewed by 1661

Editor

Prof. Dr. Athanasios Nikolaidis

E-Mail Website
Guest Editor
Department of Informatics, Computer and Telecommunications Engineering, International Hellenic University, Terma Magnesias Str., 62124 Serres, Greece
Interests: multimedia systems; digital image processing; digital signal processing; computer vision; computer graphics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advent of Industry 4.0, it has become more and more important to be able to collect user responses from different modalities. To this end, a variety of smart sensors have been developed.

Notable examples of smart sensors that have been utilized in several fields are sensors that monitor environmental conditions (e.g., temperature, pressure, humidity), sensors aimed at automation and maintenance (e.g., accelerometers, gyroscopes, magnetometers), and sensors used for the IoT (e.g., RFID and NFC sensors).

Among the plethora of available smart sensors, those that are involved in the process of face and gesture recognition are of notable importance, such as ToF sensors, smart cameras, and biometric sensors. These form an integral part in applications such as biometric authentication, fraud detection, contactless patient monitoring, user emotion recognition, and gesture-based UIs for gaming, among others.

Academics and researchers are invited to present their work and research results concerning the challenges of using smart sensors in face and gesture recognition, such as recognition accuracy and speed improvement, integration in consumer devices, and AI-driven enhancement of existing sensors, aiming at fields of application including healthcare, HMI, and automation. We look forward to your contributions.

Dr. Athanasios Nikolaidis
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors 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 2600 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

  • biometric authentication
  • computer vision
  • edge AI processing
  • infrared depth sensing
  • machine learning
  • gesture-based control
  • time-of-flight (ToF) sensors
  • emotion recognition

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 2048 KB  
Article
RPT-Mamba: A Range-Aware Physical Token Mamba Network for Far-Field mmWave Radar Gesture Recognition
by Yitong Shi, Pei Peng and Zhiyuan Wang
Sensors 2026, 26(13), 4122; https://doi.org/10.3390/s26134122 - 30 Jun 2026
Viewed by 248
Abstract
Millimeter-wave (mmWave) radar provides a privacy-preserving and illumination-robust sensing modality for contactless gesture recognition. However, sparse radar point clouds degrade substantially as sensing distance increases: the number of valid detections decreases, echo intensity attenuates, and Doppler-related motion cues become less reliable. Such range-induced [...] Read more.
Millimeter-wave (mmWave) radar provides a privacy-preserving and illumination-robust sensing modality for contactless gesture recognition. However, sparse radar point clouds degrade substantially as sensing distance increases: the number of valid detections decreases, echo intensity attenuates, and Doppler-related motion cues become less reliable. Such range-induced degradation leads to a distribution shift between near-range training samples and far-field test samples, making it difficult for models trained at short distances to generalize to unseen longer distances. Existing point-cloud gesture recognition methods usually treat radar detections as generic sparse point sequences and rarely model distance-related point loss, echo attenuation, and physical-attribute unreliability explicitly. This work introduces RPT-Mamba, a range-aware physical token Mamba network for sparse mmWave radar point cloud sequences. RPT-Mamba constructs physical point tokens from spatial coordinates, Doppler velocity, echo intensity, point-level range, and sample-level range information. During training, a range-aware stochastic degradation strategy adaptively removes points and masks dynamic attributes according to the estimated sensing distance, while a context-guided attribute reconstruction objective recovers masked Doppler and intensity attributes from spatial and frame-level context. A bidirectional Mamba temporal encoder then models long-range gesture dynamics over frame tokens. On the public mTransSee dataset, RPT-Mamba achieves 92.09% accuracy and 92.04% Macro-F1 under the random split protocol, and 85.34% accuracy and 84.77% Macro-F1 under a challenging near-to-far protocol, exceeding point-cloud, radar-gesture, Transformer, and Mamba baselines. Full article
(This article belongs to the Special Issue Smart Sensors and Imaging for Face and Gesture Recognition)
►▼ Show Figures

Figure 1

22 pages, 12841 KB  
Article
Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation
by Hao Li, Yuyang Feng, Xin Zhao, Xuan Li and Tao Zhang
Sensors 2026, 26(12), 3968; https://doi.org/10.3390/s26123968 - 22 Jun 2026
Viewed by 451
Abstract
Person re-identification (re-ID) aims to match pedestrian images across disjoint camera views. Existing multi-source unsupervised domain adaptation (UDA) re-ID methods still face two critical issues: they fail to effectively balance domain-invariant feature learning and domain-specific style preservation and cannot adequately model the implicit [...] Read more.
Person re-identification (re-ID) aims to match pedestrian images across disjoint camera views. Existing multi-source unsupervised domain adaptation (UDA) re-ID methods still face two critical issues: they fail to effectively balance domain-invariant feature learning and domain-specific style preservation and cannot adequately model the implicit correlations among diverse source domains, resulting in limited cross-domain generalization performance. To address these challenges, this paper proposes a novel multi-source UDA re-ID framework equipped with a Mixture of Experts feature extraction (MEFE) network and a Graph-Based Relation (GBR) module. Specifically, the MEFE network integrates mixed Instance and Batch Normalization (MIBN) to extract robust domain-invariant features, while the embedded domain-specific style information (DSI) module compensates for lost domain-specific style details at the feature level. Furthermore, the cascaded Graph Attention and Graph Convolution Networks (GATs/GCNs) in the GBR module adaptively explore implicit feature correlations and achieve effective multi-source feature fusion. Center maximum mean discrepancy loss is adopted to further reduce cross-domain distribution discrepancies. Extensive experiments on large-scale datasets demonstrate that the proposed method achieves state-of-the-art performance and substantially outperforms mainstream UDA re-ID approaches. Full article
(This article belongs to the Special Issue Smart Sensors and Imaging for Face and Gesture Recognition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop