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Facial-Based Emotion Recognition: Challenges and Advances in Computer Vision
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Facial-Based Emotion Recognition: Challenges and Advances in Computer Vision

A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 May 2026) | Viewed by 2236

Editors

Dr. Jingting Li

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Guest Editor
Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
Interests: affective computing; computer vision; pattern recognition
Prof. Dr. Moi Hoon Yap

E-Mail Website
Guest Editor
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
Interests: computer vision; deep learning; image/video processing of faces; gesture analysis; medical image analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaohua Huang

E-Mail Website
Guest Editor
Oulu School, Nanjing Institute of Technology, Nanjing, China
Interests: affective computing; computer vision; image processing; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Sirui Zhao

E-Mail Website1 Website2
Guest Editor
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
Interests: human-centered affective computing; multimodal understanding; human-computer interactions

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the latest advancements in emotion computing based on facial cues. Facial information plays a crucial role in social interactions, including expressions, micro-expressions, action units, gaze, and more. The Special Issue focuses on utilizing cutting-edge computer vision technologies to recognize emotions based on these facial cues. The scope of applications is broad and includes, but is not limited to, emotion recognition in social settings, psychological disorder diagnoses such as depression, medical applications like pain detection, and the generation of realistic facial expressions for digital avatars and robots. Furthermore, we encourage research on sample diversity, including studies that consider a wide range of demographic factors, such as age and nationality, which may influence emotion recognition technologies. In addition to visible facial cues, we also welcome the integration of other modalities, such as facial thermography, remote heart rate estimation, and the use of contextual information, like text, for emotion recognition, especially in the context of the growing capabilities of large language models. Overall, this Special Issue aims to advance the development of intelligent systems capable of truly understanding human emotions through both behavioral and modal facial cues across diverse application scenarios.

The scope of this Special Issue includes, but is not limited to, the following:

  • Facial expression /micro-expression based emotion recognition;
  • Facial action unit detection;
  • Multimodal emotion recognition based on facial cues;
  • In-the-wild emotion computing;
  • Emotion recognition in different application scenarios;
  • RPPG for emotion recognition;
  • Contextual emotion recognition using text and visual cues;
  • Realistic facial expression generation for digital humans and robots;
  • Other emotion recognition technologies.

In today's rapidly advancing field of computer vision, understanding individual emotions is a critical challenge for the development of intelligent systems. The face, as a key cue for expressing emotions, plays a vital role in emotional communication. Exploring facial-based emotion computing technologies can significantly contribute to intelligent analysis in areas such as psychological health screening and emotional interactions with digital humans and robots.

Dr. Jingting Li
Prof. Dr. Moi Hoon Yap
Prof. Dr. Xiaohua Huang
Dr. Sirui Zhao
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 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. Electronics 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.

Keywords

  • affective computing
  • facial cues
  • emotion recognition
  • computer vision

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Published Papers (2 papers)

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Research

19 pages, 2125 KB  
Article
PAST: Prior-Aware Sparse Transformer for Micro-Expression Recognition
by Jiateng Liu, Tianchen Zhou, Hengcan Shi, Yining Zhao, Zedong Liu, Yingtian Yu and Liming Liu
Electronics 2026, 15(15), 3321; https://doi.org/10.3390/electronics15153321 - 28 Jul 2026
Viewed by 357
Abstract
Micro-expression recognition (MER) has a lot of applications in lie detection, education, healthcare, etc., as involuntary micro-expressions (MEs) may provide subtle facial cues associated with affective responses. With the development of deep learning, many studies have recently employed Vision Transformers (ViTs) to investigate [...] Read more.
Micro-expression recognition (MER) has a lot of applications in lie detection, education, healthcare, etc., as involuntary micro-expressions (MEs) may provide subtle facial cues associated with affective responses. With the development of deep learning, many studies have recently employed Vision Transformers (ViTs) to investigate MER, since ViTs show promising performance in various visual domains due to their excellent local–global modeling ability. However, such methods confront two fundamental challenges: First, fine-grained visual features are needed to capture the subtle facial movements of MEs, which ViTs relatively fall short on due to coarse patch resolution constrained by their quadratic complexity. Second, the data-intensive nature of ViTs impedes effective learning given the limited scale of ME data. To overcome the aforementioned limitations of using ViTs for MER, we propose the Prior-aware Sparse Transformer (PAST), a novel Transformer-based architecture integrating spatial and semantic prior knowledge synergistically into a sparse attention mechanism, enabling linear-complexity processing of large amounts of fine-grained features. Specifically, we first designed an extraction algorithm to generate a representative set of motion-intensive Principal Anchors, which are used to guide the model’s focus on biologically critical regions during sampling. Second, we introduced the Semantic Dictionary, which was trained with a carefully designed self-contrastive loss to embed task-invariant discriminative semantics of the anchors. Such global semantics further modulate patch sampling and attention weighting in the sparse attention procedure, achieving better training performance with limited ME data. Extensive evaluations on MEGC and CD6ME protocols demonstrate state-of-the-art performance, validating PAST’s efficacy for MER. Full article
(This article belongs to the Special Issue Facial-Based Emotion Recognition: Challenges and Advances in Computer Vision)
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18 pages, 2280 KB  
Article
Head-Movement-Robust Micro-Expression Detection Method via 3D Motion Correction and Transformers
by Keyi Feng, Fake Jiang, Shucheng Huang and Mingxing Li
Electronics 2026, 15(9), 1836; https://doi.org/10.3390/electronics15091836 - 26 Apr 2026
Viewed by 601
Abstract
In micro-expression detection, head movements may seriously interfere with subtle and transient micro-expression signals, significantly limiting detection performance. Therefore, we propose a robust detection framework that integrates 3D motion correction and a Transformer network. This framework adopts a two-stage design. In the first [...] Read more.
In micro-expression detection, head movements may seriously interfere with subtle and transient micro-expression signals, significantly limiting detection performance. Therefore, we propose a robust detection framework that integrates 3D motion correction and a Transformer network. This framework adopts a two-stage design. In the first stage, a depth-weighted optical flow method is proposed to decompose and suppress head motion in three-dimensional space and extract anti-interference temporal optical flow features. In the second stage, a Transformer-based encoder is used to model and classify feature sequences by leveraging its capability for global feature modeling integrated with positional information. The experimental results on the CASME3-PartC and MEVIEW datasets show that the proposed method achieves F1-scores of 0.151 and 0.326, respectively, outperforming existing methods and achieving the current optimal performance. Cross-dataset experiments further validated the good generalization ability of this method. Full article
(This article belongs to the Special Issue Facial-Based Emotion Recognition: Challenges and Advances in Computer Vision)
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