Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Sensing Technologies Applied in Human Emotion and Facial Expression 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

Sensing Technologies Applied in Human Emotion and Facial Expression Recognition

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

Deadline for manuscript submissions: 25 November 2025 | Viewed by 2000

Special Issue Editors

Dr. Tong Chen

E-Mail Website
Guest Editor
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Interests: signal processing & image processing; affective computing; biomedical engineering
Prof. Dr. Hongying Meng

E-Mail Website
Guest Editor
Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK
Interests: image processing; artificial intelligence; signal processing; affective computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed significant advancements in sensing technologies for human emotions, which primarily rely on analyzing facial expressions, voice, body language, and physiological signals to accurately recognize and understand human emotional states. These technologies will provide more intelligent and personalized solutions for fields such as human–computer interaction, healthcare, education, customer service, etc.

This Special Issue aims to collate original research and review articles focusing on recent progress, technologies, approaches, applications, and new obstacles in the field of human emotion and facial expression recognition.

Dr. Tong Chen
Prof. Dr. Hongying Meng
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. 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

  • emotion recognition and understanding
  • intent recognition
  • facial expression recognition
  • micro-expression recognition
  • masked facial expression recognition
  • stress recognition
  • physiological signal
  • multi-modal signals
  • wearable sensors
  • imaging sensors
  • non-contact sensing for physiological signal measurement
  • non-contact sensing for 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 (5 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

20 pages, 3616 KiB  
Article
Res-RBG Facial Expression Recognition in Image Sequences Based on Dual Neural Networks
by Xiangwei Mou, Yongfu Song, Xiuping Xie, Mingxuan You and Rijun Wang
Sensors 2025, 25(12), 3829; https://doi.org/10.3390/s25123829 - 19 Jun 2025
Viewed by 202
Abstract
Facial expressions involve dynamic changes, and facial expression recognition based on static images struggles to capture the temporal information inherent in these dynamic changes. The resultant degradation in real-world performance critically impedes the integration of facial expression recognition systems into intelligent sensing applications. [...] Read more.
Facial expressions involve dynamic changes, and facial expression recognition based on static images struggles to capture the temporal information inherent in these dynamic changes. The resultant degradation in real-world performance critically impedes the integration of facial expression recognition systems into intelligent sensing applications. Therefore, this paper proposes a facial expression recognition method for image sequences based on the fusion of dual neural networks (ResNet and residual bidirectional GRU—Res-RBG). The model proposed in this paper achieves recognition accuracies of 98.10% and 88.64% on the CK+ and Oulu-CASIA datasets, respectively. Moreover, the model has a parameter size of only 64.20 M. Compared to existing methods for image sequence-based facial expression recognition, the approach presented in this paper demonstrates certain advantages, indicating strong potential for future edge sensor deployment. Full article
(This article belongs to the Special Issue Sensing Technologies Applied in Human Emotion and Facial Expression Recognition)
►▼ Show Figures

Figure 1

34 pages, 18851 KiB  
Article
Dual-Branch Multi-Dimensional Attention Mechanism for Joint Facial Expression Detection and Classification
by Cheng Peng, Bohao Li, Kun Zou, Bowen Zhang, Genan Dai and Ah Chung Tsoi
Sensors 2025, 25(12), 3815; https://doi.org/10.3390/s25123815 - 18 Jun 2025
Viewed by 224
Abstract
This paper addresses the central issue arising from the (SDAC) of facial expressions, namely, to balance the competing demands of good global features for detection, and fine features for good facial expression classifications by replacing the feature extraction part of the “neck” network [...] Read more.
This paper addresses the central issue arising from the (SDAC) of facial expressions, namely, to balance the competing demands of good global features for detection, and fine features for good facial expression classifications by replacing the feature extraction part of the “neck” network in the feature pyramid network in the You Only Look Once X (YOLOX) framework with a novel architecture involving three attention mechanisms—batch, channel, and neighborhood—which respectively explores the three input dimensions—batch, channel, and spatial. Correlations across a batch of images in the individual path of the dual incoming paths are first extracted by a self attention mechanism in the batch dimension; these two paths are fused together to consolidate their information and then split again into two separate paths; the information along the channel dimension is extracted using a generalized form of channel attention, an adaptive graph channel attention, which provides each element of the incoming signal with a weight that is adapted to the incoming signal. The combination of these two paths, together with two skip connections from the input to the batch attention to the output of the adaptive channel attention, then passes into a residual network, with neighborhood attention to extract fine features in the spatial dimension. This novel dual path architecture has been shown experimentally to achieve a better balance between the competing demands in an SDAC problem than other competing approaches. Ablation studies enable the determination of the relative importance of these three attention mechanisms. Competitive results are obtained on two non-aligned face expression recognition datasets, RAF-DB and SFEW, when compared with other state-of-the-art methods. Full article
(This article belongs to the Special Issue Sensing Technologies Applied in Human Emotion and Facial Expression Recognition)
►▼ Show Figures

Figure 1

26 pages, 6016 KiB  
Article
Facial Landmark-Driven Keypoint Feature Extraction for Robust Facial Expression Recognition
by Jaehyun So and Youngjoon Han
Sensors 2025, 25(12), 3762; https://doi.org/10.3390/s25123762 - 16 Jun 2025
Viewed by 273
Abstract
Facial expression recognition (FER) is a core technology that enables computers to understand and react to human emotions. In particular, the use of face alignment algorithms as a preprocessing step in image-based FER is important for accurately normalizing face images in terms of [...] Read more.
Facial expression recognition (FER) is a core technology that enables computers to understand and react to human emotions. In particular, the use of face alignment algorithms as a preprocessing step in image-based FER is important for accurately normalizing face images in terms of scale, rotation, and translation to improve FER accuracy. Recently, FER studies have been actively leveraging feature maps computed by face alignment networks to enhance FER performance. However, previous studies were limited in their ability to effectively apply information from specific facial regions that are important for FER, as they either only used facial landmarks during the preprocessing step or relied solely on the feature maps from the face alignment networks. In this paper, we propose the use of Keypoint Features extracted from feature maps at the coordinates of facial landmarks. To effectively utilize Keypoint Features, we further propose a Keypoint Feature regularization method using landmark perturbation for robustness, and an attention mechanism that emphasizes all Keypoint Features using representative Keypoint Features derived from a nasal base landmark, which carries information for the whole face, to improve performance. We performed experiments on the AffectNet, RAF-DB, and FERPlus datasets using a simply designed network to validate the effectiveness of the proposed method. As a result, the proposed method achieved a performance of 68.17% on AffectNet-7, 64.87% on AffectNet-8, 93.16% on RAF-DB, and 91.44% on FERPlus. Furthermore, the network pretrained on AffectNet-8 had improved performances of 94.04% on RAF-DB and 91.66% on FERPlus. These results demonstrate that the proposed Keypoint Features can achieve comparable results to those of the existing methods, highlighting their potential for enhancing FER performance through the effective utilization of key facial region features. Full article
(This article belongs to the Special Issue Sensing Technologies Applied in Human Emotion and Facial Expression Recognition)
►▼ Show Figures

Graphical abstract

16 pages, 23492 KiB  
Article
CAGNet: A Network Combining Multiscale Feature Aggregation and Attention Mechanisms for Intelligent Facial Expression Recognition in Human-Robot Interaction
by Dengpan Zhang, Wenwen Ma, Zhihao Shen and Qingping Ma
Sensors 2025, 25(12), 3653; https://doi.org/10.3390/s25123653 - 11 Jun 2025
Viewed by 375
Abstract
The development of Facial Expression Recognition (FER) technology has significantly enhanced the naturalness and intuitiveness of human-robot interaction. In the field of service robots, particularly in applications such as production assistance, caregiving, and daily service communication, efficient FER capabilities are crucial. However, existing [...] Read more.
The development of Facial Expression Recognition (FER) technology has significantly enhanced the naturalness and intuitiveness of human-robot interaction. In the field of service robots, particularly in applications such as production assistance, caregiving, and daily service communication, efficient FER capabilities are crucial. However, existing Convolutional Neural Network (CNN) models still have limitations in terms of feature representation and recognition accuracy for facial expressions. To address these challenges, we propose CAGNet, a novel network that combines multiscale feature aggregation and attention mechanisms. CAGNet employs a deep learning-based hierarchical convolutional architecture, enhancing the extraction of features at multiple scales through stacked convolutional layers. The network integrates the Convolutional Block Attention Module (CBAM) and Global Average Pooling (GAP) modules to optimize the capture of both local and global features. Additionally, Batch Normalization (BN) layers and Dropout techniques are incorporated to improve model stability and generalization. CAGNet was evaluated on two standard datasets, FER2013 and CK+, and the experiment results demonstrate that the network achieves accuracies of 71.52% and 97.97%, respectively, in FER. These results not only validate the effectiveness and superiority of our approach but also provide a new technical solution for FER. Furthermore, CAGNet offers robust support for the intelligent upgrade of service robots. Full article
(This article belongs to the Special Issue Sensing Technologies Applied in Human Emotion and Facial Expression Recognition)
►▼ Show Figures

Figure 1

22 pages, 23958 KiB  
Article
A Lightweight Dual-Stream Network with an Adaptive Strategy for Efficient Micro-Expression Recognition
by Xinyu Liu, Ju Zhou, Feng Chen, Shigang Li, Hanpu Wang, Yingjuan Jia and Yuhao Shan
Sensors 2025, 25(9), 2866; https://doi.org/10.3390/s25092866 - 1 May 2025
Cited by 1 | Viewed by 398
Abstract
Micro-expressions (MEs), characterized by their brief duration and subtle facial muscle movements, pose significant challenges for accurate recognition. These ultra-fast signals, typically captured by high-speed vision sensors, require specialized computational methods to extract spatio-temporal features effectively. In this study, we propose a lightweight [...] Read more.
Micro-expressions (MEs), characterized by their brief duration and subtle facial muscle movements, pose significant challenges for accurate recognition. These ultra-fast signals, typically captured by high-speed vision sensors, require specialized computational methods to extract spatio-temporal features effectively. In this study, we propose a lightweight dual-stream network with an adaptive strategy for efficient ME recognition. Firstly, a motion magnification network based on transfer learning is employed to magnify the motion states of facial muscles in MEs. This process can generate additional samples, thereby expanding the training set. To effectively capture the dynamic changes of facial muscles, dense optical flow is extracted from the onset frame and the magnified apex frame, thereby obtaining magnified dense optical flow (MDOF). Subsequently, we design a dual-stream spatio-temporal network (DSTNet), using the magnified apex frame and MDOF as inputs for the spatial and temporal streams, respectively. An adaptive strategy that dynamically adjusts the magnification factor based on the top-1 confidence is introduced to enhance the robustness of DSTNet. Experimental results show that our proposed method outperforms existing methods in terms of F1-score on the SMIC, CASME II, SAMM, and composite dataset, as well as in cross-dataset tasks. Adaptive DSTNet significantly enhances the handling of sample imbalance while demonstrating robustness and featuring a lightweight design, indicating strong potential for future edge sensor deployment. Full article
(This article belongs to the Special Issue Sensing Technologies Applied in Human Emotion and Facial Expression 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 1-5
Back to TopTop