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Journal articles on the topic 'Micro Expression Recognition'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Kasia, Wezowski. "Assessing Effect of Learning micro-Expressions by Analyzing Images and Video Training programs." American Based Research Journal 6, no. 8 (2017): 36–48. https://doi.org/10.5281/zenodo.3445783.

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<em>Micro-expressions are reliable indicators of true emotions and therefore the ability to read them is important in many different professional areas, such as lie detection, criminal study, national security, marketing, sales and service, medical care, and law. The ability to identify micro-expressions can be enhanced with training and practice. Different tools for assessing this ability can be employed in training people to recognize micro-expressions. We conducted two studies on two different samples to examine the effectiveness of the new tool, the Micro Expressions Training Videos progra

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Wang, Chongyang, Min Peng, Tao Bi, and Tong Chen. "Micro-attention for micro-expression recognition." Neurocomputing 410 (October 2020): 354–62. http://dx.doi.org/10.1016/j.neucom.2020.06.005.

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Zhang, Peng, Xianye Ben, Rui Yan, Chen Wu, and Chang Guo. "Micro-expression recognition system." Optik 127, no. 3 (2016): 1395–400. http://dx.doi.org/10.1016/j.ijleo.2015.10.217.

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Zhao, Yue, and Jiancheng Xu. "A Convolutional Neural Network for Compound Micro-Expression Recognition." Sensors 19, no. 24 (2019): 5553. http://dx.doi.org/10.3390/s19245553.

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Human beings are particularly inclined to express real emotions through micro-expressions with subtle amplitude and short duration. Though people regularly recognize many distinct emotions, for the most part, research studies have been limited to six basic categories: happiness, surprise, sadness, anger, fear, and disgust. Like normal expressions (i.e., macro-expressions), most current research into micro-expression recognition focuses on these six basic emotions. This paper describes an important group of micro-expressions, which we call compound emotion categories. Compound micro-expressions

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Yang, Yunxuan. "Micro-expressions: A Study of Basic Reading and The Influencing Factors on Production and Recognition." Journal of Education, Humanities and Social Sciences 26 (March 2, 2024): 1048–53. http://dx.doi.org/10.54097/y71ea179.

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In response to nonverbal communication in psychology, micro-expression research has gained widespread attention as a relatively new but growing field. As technology advances, researchers have developed computer-based tools and software to assist in analyzing micro-expressions. These tools use facial recognition and machine learning algorithms to more accurately detect and categorize micro-expressions. This article introduces the definition and basic types of micro-expressions by integrating related literature and further analyzes the four influencing factors including gender, cultural differen

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Varanka, Tuomas, Wei Peng, and Guoying Zhao. "Micro-Expression Recognition with Noisy Labels." Electronic Imaging 2021, no. 11 (2021): 157–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.11.hvei-157.

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Facial micro-expressions are quick, involuntary and low intensity facial movements. An interest in detecting and recognizing micro-expressions arises from the fact that they are able to show person’s genuine hidden emotions. The small and rapid facial muscle movements are often too difficult for a human to not only spot the occurring micro-expression but also be able to recognize the emotion correctly. Recently, a focus on developing better micro-expression recognition methods has been on models and architectures. However, we take a step back and go to the root of task, the data. We thoroughly

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Yu, Yali, Zonghou Huang, and Jinchan Liang. "A Review of Facial Micro Expression Recognition." Frontiers in Computing and Intelligent Systems 9, no. 3 (2024): 46–48. http://dx.doi.org/10.54097/w1pzar38.

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With facial expression recognition gradually becoming a hot topic in the fields of image processing and artificial intelligence research, more and more scholars are paying attention to the fact that the micro expressions instantly revealed on the face can better reflect human inner emotions and thoughts. In this paper, firstly, the research status of micro expression and the commonly used micro expression datasets are described, and the advantages and disadvantages of each dataset are analyzed. Then the feature extraction of micro expression is analyzed from the two algorithms. Finally, the ap

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C, Gnanaprakasam. "Attention Residual Network for Micro-expression Recognition Using Image Analysis." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1261–72. http://dx.doi.org/10.5373/jardcs/v12sp7/20202226.

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Chaya, Kengo. "The facial expression recognition which reflects micro-expressions." Proceedings of the Annual Convention of the Japanese Psychological Association 82 (September 25, 2018): 2AM—095–2AM—095. http://dx.doi.org/10.4992/pacjpa.82.0_2am-095.

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Liu, Xin, Fugang Wang, Hui Zeng, Yile Chen, Liang Zheng, and Junming Chen. "PRNet: A Priori Embedded Network for Real-World Blind Micro-Expression Recognition." Mathematics 13, no. 5 (2025): 749. https://doi.org/10.3390/math13050749.

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Micro-expressions, fleeting and often unnoticed facial cues, hold the key to uncovering concealed emotions, offering significant implications for understanding emotions, cognition, and psychological processes. However, micro-expression information capture presents challenges due to its instantaneous and subtle nature. Furthermore, it is affected by unpredictable degradation factors such as device performance and weather, and model degradation issues persist in real scenarios, and directly training deep networks or introducing image restoration networks yields unsatisfactory results, hindering

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Irawan, Budhi, Rinaldi Munir, Nugraha Priya Utama, and Ayu Purwarianti. "Improving the Accuracy of Facial Micro-Expression Recognition: Spatio-Temporal Deep Learning with Enhanced Data Augmentation and Class Balancing." Interdisciplinary Journal of Information, Knowledge, and Management 19 (2024): 031. http://dx.doi.org/10.28945/5386.

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Aim/Purpose: This study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framewo

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Qi, Fengyu, Chuanying Yang, Bao Shi, and Shaoying Ma. "Micro-expression Recognition Based on DCBAM-EfficientNet Model." Journal of Physics: Conference Series 2504, no. 1 (2023): 012062. http://dx.doi.org/10.1088/1742-6596/2504/1/012062.

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Abstract To address the problems of low accuracy of existing deep learning-based micro-expression recognition models, numerous network parameters, and the difficulty of mobile deployment of micro-expression recognition models, this paper proposes DCBAM-EfficientNet, a micro-expression recognition model that uses the lightweight network EfficientNet as the backbone network and incorporates the attention module. The network can guarantee the accuracy of micro-expression recognition with relatively few network parameters. The attention mechanism allows the more expressive micro-expression feature

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Zhao, Yue, and Jiancheng Xu. "Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition." Applied Sciences 8, no. 10 (2018): 1811. http://dx.doi.org/10.3390/app8101811.

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Micro expressions are usually subtle and brief facial expressions that humans use to hide their true emotional states. In recent years, micro-expression recognition has attracted wide attention in the fields of psychology, mass media, and computer vision. The shortest micro expression lasts only 1/25 s. Furthermore, different from macro-expressions, micro-expressions have considerable low intensity and inadequate contraction of the facial muscles. Based on these characteristics, automatic micro-expression detection and recognition are great challenges in the field of computer vision. In this p

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Davison, Adrian, Walied Merghani, and Moi Yap. "Objective Classes for Micro-Facial Expression Recognition." Journal of Imaging 4, no. 10 (2018): 119. http://dx.doi.org/10.3390/jimaging4100119.

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Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset (Chinese Academy of Sciences Micro-expression II) are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP (Local Binary Patter

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Min, Koo Sie, Mohd Asyraf Zulkifley, and Nor Azwan Mohamed Kamari. "A Review of Automated Micro-expression Analysis." Jurnal Kejuruteraan 34, no. 5 (2022): 763–75. http://dx.doi.org/10.17576/jkukm-2022-34(5)-02.

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Micro-expression is a type of facial expression that is manifested for a very short duration. It is difficult to recognize the expression manually because it involves very subtle facial movements. Such expressions often occur unconsciously, and therefore are defined as a basis to help identify the real human emotions. Hence, an automated approach to micro-expression recognition has become a popular research topic of interest recently. Historically, the early researches on automated micro-expression have utilized traditional machine learning methods, while the more recent development has focuse

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Xie, Tingxuan, Guoquan Sun, Hao Sun, Qiang Lin, and Xianye Ben. "Decoupling facial motion features and identity features for micro-expression recognition." PeerJ Computer Science 8 (November 14, 2022): e1140. http://dx.doi.org/10.7717/peerj-cs.1140.

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Background Micro-expression is a kind of expression produced by people spontaneously and unconsciously when receiving stimulus. It has the characteristics of low intensity and short duration. Moreover, it cannot be controlled and disguised. Thus, micro-expression can objectively reflect people’s real emotional states. Therefore, automatic recognition of micro-expressions can help machines better understand the users’ emotion, which can promote human-computer interaction. What’s more, micro-expression recognition has a wide range of applications in fields like security systems and psychological

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Wang, Shiqi, Suen Guan, Hui Lin, Jianming Huang, Fei Long, and Junfeng Yao. "Micro-Expression Recognition Based on Optical Flow and PCANet+." Sensors 22, no. 11 (2022): 4296. http://dx.doi.org/10.3390/s22114296.

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Micro-expressions are rapid and subtle facial movements. Different from ordinary facial expressions in our daily life, micro-expressions are very difficult to detect and recognize. In recent years, due to a wide range of potential applications in many domains, micro-expression recognition has aroused extensive attention from computer vision. Because available micro-expression datasets are very small, deep neural network models with a huge number of parameters are prone to over-fitting. In this article, we propose an OF-PCANet+ method for micro-expression recognition, in which we design a spati

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Huang, Wei. "Elderly Depression Recognition Based on Facial Micro-Expression Extraction." Traitement du Signal 38, no. 4 (2021): 1123–30. http://dx.doi.org/10.18280/ts.380423.

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Depression leads to a high suicide rate and a high death rate. But the disease can be cured if recognized in time. At present, there are only a few low-precision methods for recognizing mental health or mental disorder. Therefore, this paper attempts to recognize elderly depression by extracting facial micro-expressions. Firstly, a micro-expression recognition model was constructed for elderly depression recognition. Then, a jump connection structure and a feature fusion module were introduced to VGG-16 model, realizing the extraction and classification of micro-expression features. After that

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Yang, Huanjing, Shibao Sun, and Jingyu Chen. "Deep Learning-Based Micro-Expression Recognition Algorithm Research." International Journal of Computer Science and Information Technology 2, no. 1 (2024): 59–70. http://dx.doi.org/10.62051/ijcsit.v2n1.08.

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In order to improve the accuracy and speed of micro-expressions, a modified model based on densenet and eca is proposed. Microfacial expression is a brief, weak facial change, its characteristics are similar, dense, difficult to extract and identify, and the improved model can be adapted to the characteristics and location of the interest. In particular, the eca attention module was added after the densenet model, using the densenet network to extract the rich characteristics of micro-expressions, and the eca attention module to recalibrate the feature channel and focus on the more subtle expr

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Elham, Zarezadeh, and Rezaeian Mehdi. "BRAIN Journal - Micro Expression Recognition Using the Eulerian Video Magnification Method." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 7, no. 3 (2016): 43–54. https://doi.org/10.5281/zenodo.1044975.

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ABSTRACT In this paper we propose a new approach for facial micro expressions recognition. For this purpose the Eulerian Video Magnification (EVM) method is used to retrieve the subtle motions of the face. The results of this method are obtained as in the magnified images sequence. In this study the numerical tests are performed on two databases: Spontaneous Micro expression (SMIC) and Category and Sourcing Managers Executive (CASME). We evaluate our proposed method in two phases using the eigenface method. In phase 1 we recognize the type of a micro expression, for example emotional versus un

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Wu, Junjie, Jianfeng Xu, Deyu Lin, and Min Tu. "Optical Flow Filtering-Based Micro-Expression Recognition Method." Electronics 9, no. 12 (2020): 2056. http://dx.doi.org/10.3390/electronics9122056.

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The recognition accuracy of micro-expressions in the field of facial expressions is still understudied, as current research methods mainly focus on feature extraction and classification. Based on optical flow and decision thinking theory, we propose a novel micro-expression recognition method, which can filter low-quality micro-expression video clips. Determined by preset thresholds, we develop two optical flow filtering mechanisms: one based on two-branch decisions (OFF2BD) and the other based on three-way decisions (OFF3WD). In OFF2BD, which use the classical binary logic to classify images,

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Zhang, Yanliang, Ying Liu, and Hao Wang. "Cross-Database Micro-Expression Recognition Exploiting Intradomain Structure." Journal of Healthcare Engineering 2021 (May 22, 2021): 1–9. http://dx.doi.org/10.1155/2021/5511509.

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Micro-expressions are unconscious, faint, short-lived expressions that appear on the faces. It can make people's understanding of psychological state and emotion more accurate. Therefore, micro-expression recognition is particularly important in psychotherapy and clinical diagnosis, which has been widely studied by researchers for the past decades. In practical applications, the micro-expression recognition samples used in training and testing are from different databases, which causes the feature distribution between the training and testing samples to be different to a large extent, resultin

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Wang, Su-Jing, Wen-Jing Yan, Xiaobai Li, et al. "Micro-Expression Recognition Using Color Spaces." IEEE Transactions on Image Processing 24, no. 12 (2015): 6034–47. http://dx.doi.org/10.1109/tip.2015.2496314.

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Zhang, Ming, Qiufang Fu, Yu-Hsin Chen, and Xiaolan Fu. "Emotional Context Influences Micro-Expression Recognition." PLoS ONE 9, no. 4 (2014): e95018. http://dx.doi.org/10.1371/journal.pone.0095018.

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Takalkar, Madhumita, Min Xu, Qiang Wu, and Zenon Chaczko. "A survey: facial micro-expression recognition." Multimedia Tools and Applications 77, no. 15 (2017): 19301–25. http://dx.doi.org/10.1007/s11042-017-5317-2.

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Lu, Hua, Kidiyo Kpalma, and Joseph Ronsin. "Motion descriptors for micro-expression recognition." Signal Processing: Image Communication 67 (September 2018): 108–17. http://dx.doi.org/10.1016/j.image.2018.05.014.

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Vishal, Dubey1 Bhavya Takkar2 and Puneet Singh Lamba 3. "Micro-Expression Recognition using 3D - CNN." Fusion: Practice and Applications (FPA) 1, no. 1 (2020): 5–14. https://doi.org/10.5281/zenodo.3825862.

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<em>Fusion: Practice and Applications (FPA)                                                    Vol. 1, No. 1, PP. 5-14, 2020</em>

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Jin, Hongmei, Ning He, Zhanli Li, and Pengcheng Yang. "Micro-expression recognition based on multi-scale 3D residual convolutional neural network." Mathematical Biosciences and Engineering 21, no. 4 (2024): 5007–31. http://dx.doi.org/10.3934/mbe.2024221.

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<abstract><p>In demanding application scenarios such as clinical psychotherapy and criminal interrogation, the accurate recognition of micro-expressions is of utmost importance but poses significant challenges. One of the main difficulties lies in effectively capturing weak and fleeting facial features and improving recognition performance. To address this fundamental issue, this paper proposed a novel architecture based on a multi-scale 3D residual convolutional neural network. The algorithm leveraged a deep 3D-ResNet50 as the skeleton model and utilized the micro-expression optic

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Zhang, Yunqiu, and Chuanlin Zhu. "The Influence of Face Masks on Micro-Expression Recognition." Behavioral Sciences 15, no. 2 (2025): 200. https://doi.org/10.3390/bs15020200.

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This study aimed to explore the influence of various mask attributes on the recognition of micro-expressions (happy, neutral, and fear) and facial favorability under different background emotional conditions (happy, neutral, and fear). The participants were asked to complete an ME (micro-expression) recognition task, and the corresponding accuracy (ACC), reaction time (RT), and facial favorability were analyzed. Results: (1) Background emotions significantly impacted the RT and ACC in micro-expression recognition, with fear backgrounds hindering performance. (2) Mask wearing, particularly opaq

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Zhao, Yue, and Jiancheng Xu. "An Improved Micro-Expression Recognition Method Based on Necessary Morphological Patches." Symmetry 11, no. 4 (2019): 497. http://dx.doi.org/10.3390/sym11040497.

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Micro-expression is a spontaneous emotional representation that is not controlled by logic. A micro-expression is both transitory (short duration) and subtle (small intensity), so it is difficult to detect in people. Micro-expression detection is widely used in the fields of psychological analysis, criminal justice and human-computer interaction. Additionally, like traditional facial expressions, micro-expressions also have local muscle movement. Psychologists have shown micro-expressions have necessary morphological patches (NMPs), which are triggered by emotion. Furthermore, the objective of

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Jia, Xitong, Xianye Ben, Hui Yuan, Kidiyo Kpalma, and Weixiao Meng. "Macro-to-micro transformation model for micro-expression recognition." Journal of Computational Science 25 (March 2018): 289–97. http://dx.doi.org/10.1016/j.jocs.2017.03.016.

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Owusu, Ebenezer, Ebenezer Komla Gavua, and Zhan Yong-Zhao. "Facial Expression Recognition – A Comprehensive Review." International Journal of Technology and Management Research 1, no. 4 (2020): 29–46. http://dx.doi.org/10.47127/ijtmr.v1i4.36.

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In this paper, we have provided a comprehensive review of modern facial expression recognition system. The history of the technology as well as the current status in terms of accomplishments and challenges has been emphasized. First, we highlighted some modern applications of the technology. The best methods of face detection, an essential component of automatic facial expression system, are also discussed. Facial Action Coding Systems- the cumulative database of research and development of micro expressions within the behavioral science are also enlightened. Then various facial expression dat

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Xie, Dr Xiaoling, and Dr Zeming Fang. "Multi-Modal Emotional Understanding in AI Virtual Characters: Integrating Micro-Expression-Driven Feedback within Context-Aware Facial Micro-Expression Processing Systems." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 3 (2024): 474–500. http://dx.doi.org/10.58346/jowua.2024.i3.031.

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To engage users, AI Virtual Characters must comprehend emotions. The paper develops and evaluates Chinese-specific context-aware facial micro-expression processing algorithms and feedback mechanisms to improve AI virtual characters' multi-modal emotional comprehension in Chinese culture. Specialized algorithms were used to collect and evaluate Chinese micro-expressions and assess AI virtual characters' emotional comprehension in user interactions. Chinese participants of various ages, genders, and places were recruited for micro-expression recognition to ensure cultural inclusion. A comprehens

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Chen, Boyu, Zhihao Zhang, Nian Liu, Yang Tan, Xinyu Liu, and Tong Chen. "Spatiotemporal Convolutional Neural Network with Convolutional Block Attention Module for Micro-Expression Recognition." Information 11, no. 8 (2020): 380. http://dx.doi.org/10.3390/info11080380.

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A micro-expression is defined as an uncontrollable muscular movement shown on the face of humans when one is trying to conceal or repress his true emotions. Many researchers have applied the deep learning framework to micro-expression recognition in recent years. However, few have introduced the human visual attention mechanism to micro-expression recognition. In this study, we propose a three-dimensional (3D) spatiotemporal convolutional neural network with the convolutional block attention module (CBAM) for micro-expression recognition. First image sequences were input to a medium-sized conv

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Xia, Qun, and Xiaofeng Ding. "Facial Micro-expression Recognition Algorithm Based on Big Data." Journal of Physics: Conference Series 2066, no. 1 (2021): 012023. http://dx.doi.org/10.1088/1742-6596/2066/1/012023.

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Abstract The 21st century is the era of big data. All aspects of society, from facial expressions to national defense and military, will generate massive amounts of data. Facial expression recognition technology, as a new technology spawned in the era of big data, has broad applications The prospects are widely used in intelligent transportation, assisted medical care, distance education, interactive games and public safety. In recent years, it has attracted more scholars’ attention and has become another research hotspot in the field of computer vision and machine learning. The purpose of thi

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Ma, Lin, Wenfeng Chen, Xiaolan Fu, and Tongtong Wang. "Emotional expression and micro-expression recognition in depressive patients." Chinese Science Bulletin 63, no. 20 (2018): 2048–56. http://dx.doi.org/10.1360/n972017-01272.

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Zhang, Peiying, Ruixin Wang, Jia Luo, and Lei Shi. "Micro-Expression Recognition Algorithm Using Regions of Interest and the Weighted ArcFace Loss." Electronics 14, no. 1 (2024): 2. https://doi.org/10.3390/electronics14010002.

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Micro-expressions often reveal more genuine emotions but are challenging to recognize due to their brief duration and subtle amplitudes. To address these challenges, this paper introduces a micro-expression recognition method leveraging regions of interest (ROIs). Firstly, four specific ROIs are selected based on an analysis of the optical flow and relevant action units activated during micro-expressions. Secondly, effective feature extraction is achieved using the optical flow method. Thirdly, a block partition module is integrated into a convolutional neural network to reduce computational c

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Li, Qiuyu, Jun Yu, Toru Kurihara, Haiyan Zhang, and Shu Zhan. "Deep Convolutional Neural Network with Optical Flow for Facial Micro-Expression Recognition." Journal of Circuits, Systems and Computers 29, no. 01 (2019): 2050006. http://dx.doi.org/10.1142/s0218126620500061.

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Micro-expression is a kind of brief facial movements which could not be controlled by the nervous system. Micro-expression indicates that a person is hiding his true emotion consciously. Micro-expression recognition has various potential applications in public security and clinical medicine. Researches are focused on the automatic micro-expression recognition, because it is hard to recognize the micro-expression by people themselves. This research proposed a novel algorithm for automatic micro-expression recognition which combined a deep multi-task convolutional network for detecting the facia

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Ren, Yili, Ruidong Lu, Guan Yuan, Dashuai Hao, and Hongjue Li. "Attention-Based Spatiotemporal-Aware Network for Fine-Grained Visual Recognition." Applied Sciences 14, no. 17 (2024): 7755. http://dx.doi.org/10.3390/app14177755.

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On public benchmarks, current macro facial expression recognition technologies have achieved significant success. However, in real-life scenarios, individuals may attempt to conceal their true emotions. Conventional expression recognition often overlooks subtle facial changes, necessitating more fine-grained micro-expression recognition techniques. Different with prevalent facial expressions, weak intensity and short duration are the two main obstacles for perceiving and interpreting a micro-expression correctly. Meanwhile, correlations between pixels of visual data in spatial and channel dime

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Le, Trang Thanh Quynh, Thuong-Khanh Tran, and Manjeet Rege. "Rank-Pooling-Based Features on Localized Regions for Automatic Micro-Expression Recognition." International Journal of Multimedia Data Engineering and Management 11, no. 4 (2020): 25–37. http://dx.doi.org/10.4018/ijmdem.2020100102.

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Facial micro-expression is a subtle and involuntary facial expression that exhibits short duration and low intensity where hidden feelings can be disclosed. The field of micro-expression analysis has been receiving substantial awareness due to its potential values in a wide variety of practical applications. A number of studies have proposed sophisticated hand-crafted feature representations in order to leverage the task of automatic micro-expression recognition. This paper employs a dynamic image computation method for feature extraction so that features can be learned on certain localized fa

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Liu, Kun-Hong, Qiu-Shi Jin, Huang-Chao Xu, Yee-Siang Gan, and Sze-Teng Liong. "Micro-expression recognition using advanced genetic algorithm." Signal Processing: Image Communication 93 (April 2021): 116153. http://dx.doi.org/10.1016/j.image.2021.116153.

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Yan, Wen-Jing, Su-Jing Wang, Yong-Jin Liu, Qi Wu, and Xiaolan Fu. "For micro-expression recognition: Database and suggestions." Neurocomputing 136 (July 2014): 82–87. http://dx.doi.org/10.1016/j.neucom.2014.01.029.

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Takalkar, Madhumita A., Min Xu, and Zenon Chaczko. "Manifold feature integration for micro-expression recognition." Multimedia Systems 26, no. 5 (2020): 535–51. http://dx.doi.org/10.1007/s00530-020-00663-8.

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Ab Razak, Nur Aishah, and Shahnorbanun Sahran. "Lightweight Micro-Expression Recognition on Composite Database." Applied Sciences 13, no. 3 (2023): 1846. http://dx.doi.org/10.3390/app13031846.

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The potential of leveraging micro-expression in various areas such as security, health care and education has intensified interests in this area. Unlike facial expression, micro-expression is subtle and occurs rapidly, making it imperceptible. Micro-expression recognition (MER) on composite dataset following Micro-Expression Grand Challenge 2019 protocol is an ongoing research area with challenges stemming from demographic variety of the samples as well as small and imbalanced dataset. However, most micro-expression recognition (MER) approaches today are complex and require computationally exp

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Gan, Y. S., Sze-Teng Liong, Wei-Chuen Yau, Yen-Chang Huang, and Lit-Ken Tan. "OFF-ApexNet on micro-expression recognition system." Signal Processing: Image Communication 74 (May 2019): 129–39. http://dx.doi.org/10.1016/j.image.2019.02.005.

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Wang, Shiyuan, Xingcong Zhao, Xiaomei Zeng, et al. "Micro-expression recognition based on EEG signals." Biomedical Signal Processing and Control 86 (September 2023): 105037. http://dx.doi.org/10.1016/j.bspc.2023.105037.

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Li, Jingting, Ting Wang, and Su-Jing Wang. "Facial Micro-Expression Recognition Based on Deep Local-Holistic Network." Applied Sciences 12, no. 9 (2022): 4643. http://dx.doi.org/10.3390/app12094643.

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A micro-expression is a subtle, local and brief facial movement. It can reveal the genuine emotions that a person tries to conceal and is considered an important clue for lie detection. The micro-expression research has attracted much attention due to its promising applications in various fields. However, due to the short duration and low intensity of micro-expression movements, micro-expression recognition faces great challenges, and the accuracy still demands improvement. To improve the efficiency of micro-expression feature extraction, inspired by the psychological study of attentional reso

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Intan Puspitasari, Anton Yudhana, Dewi Eko Wati, and Syahid Al Irfan. "Recognizing Micro Expression Pattern Using Convolutional Neural Networks (CNN) Method During Emotion Regulation Training for Parents in The Pandemic Era." Proceeding of The International Conference of Inovation, Science, Technology, Education, Children, and Health 3, no. 2 (2023): 67–77. https://doi.org/10.62951/icistech.v3i2.68.

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During this pandemic, most of people’s activities are carried out through digital media. Both learning and working processes are using the video-conference platform, a platform deemed effective to facilitate the needs of distance communication. One of the limitations of using video-conference lies in difficulty in understanding emotional conditions based on solely camera video. Hence, speakers generally do not know their interlocutors’ feelings related to the materials being presented. Grounded on this issue, we examined a facial expressions-based emotion recognition tool. Micro expression is

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Tang, Bangwei. "Comparative Analysis of Convolutional Neural Networks on The Stability and Performance for Micro-Expression Recognition." Highlights in Science, Engineering and Technology 124 (February 18, 2025): 349–55. https://doi.org/10.54097/szqds112.

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Micro-expression recognition can reveal many aspects of a person, such as emotions and mental states. They have been widely used in various fields, including computer vision and natural language processing. As deep learning has become increasingly popular recently, it has started to be used for micro-expression recognition to further improve its performance. In this paper, the research focuses on three typical convolutional neural networks for micro-expression recognition: VGG16, VGG19, and MobileNetV3-Small. This research aims to compare the stability of these three convolutional neural netwo

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Choirina, Priska, Indah Martha Fitriani, Ulla Delfana Rosiani, Muhammad Nabil Mufti, Firmanda Ahmadani Arsistawa, and Pangestuti Prima Darajat. "Improved Micro-expression Recognition: An Apex Frame-Based Approach Feature Tracking and KLT." Jurnal Teknik Informatika (Jutif) 6, no. 2 (2025): 593–608. https://doi.org/10.52436/1.jutif.2025.6.2.4235.

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This research develops a real-time facial micro-expression recognition system, focusing on analyzing the onset and apex phases of micro-expression on the Spontaneous Activity and Micro-Movements (SAMM) dataset. Micro- expressions are very brief (0.04 - 0.2 seconds) facial muscle movements that often occur when a person is trying to hide emotions. The developed system aims to improve computation time efficiency and micro-expression recognition accuracy by optimizing feature extraction techniques and selecting more specific facial areas, including facial components such as eyebrows, eyes, and mo

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