Relevant bibliographies by topics / Micro Expression Recognition

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Academic literature on the topic 'Micro Expression Recognition'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Micro Expression Recognition"

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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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Dissertations / Theses on the topic "Micro Expression Recognition"

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Lu, Hua. "Video Analysis for Micro- Expression Spotting and Recognition." Thesis, Rennes, INSA, 2018. http://www.theses.fr/2018ISAR0005/document.

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Les principales contributions de cette these, en analyse d'image, portent sur l’etude des caracteristiques de reperage et de reconnaissance des micro-expressions. les approches d’analyse d’expressions faciales dans le domaine de la vision par ordinateur consistent a les detecter et a les classer dans des videos. par rapport a la macro-expression, une micro-expression induit dans une partie du visage un changement rapide durant moins d'une demi-seconde. de plus, cette subtile apparition dans une partie du visage rend difficile sa detection et sa reconnaissance. ces dernieres annees ont connu un
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Varanka, T. (Tuomas). "A comparative study of facial micro-expression recognition." Bachelor's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201909282947.

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Abstract. Facial micro-expressions are involuntary and rapid facial movements that reveal hidden emotions. Spotting and recognition of micro-expressions is a hard task even for humans due to their low magnitude and short duration compared to macro-expressions. In this thesis we look at why micro-expressions are important, datasets that contain micro-expressions for training of automatic systems, and how we can utilize modern computational methods to automatically recognize micro-expressions. Furthermore, we experiment with several representative methods in the literature and compare their perf
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Sutter, Julianne V. "ASSESSING IMPACT OF AFFECT RECOGNITION ON THERAPEUTIC RELATIONSHIP." UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_theses/14.

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Therapeutic alliance and its relationship to client nonverbal behavior, specifically facial expressions, were examined. Therapist interpretation of the client nonverbal behavior, or affect, influences the therapeutic alliance and process. Based on a sample of clients from a graduate school therapy training facility, results suggest therapist training in facial expressions, and how they relate to client emotion, improve the therapeutic alliance between therapist and client. After a micro-expression training for therapists, clients reported higher life functioning on the Outcome Rating Scale (OR
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Shreve, Matthew Adam. "Automatic Macro- and Micro-Facial Expression Spotting and Applications." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4770.

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Automatically determining the temporal characteristics of facial expressions has extensive application domains such as human-machine interfaces for emotion recognition, face identification, as well as medical analysis. However, many papers in the literature have not addressed the step of determining when such expressions occur. This dissertation is focused on the problem of automatically segmenting macro- and micro-expressions frames (or retrieving the expression intervals) in video sequences, without the need for training a model on a specific subset of such expressions. The proposed method e
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Arango, Duque Carlos. "Analysis of Micro-Expressions based on the Riesz Pyramid : Application to Spotting and Recognition." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSES062/document.

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Les micro-expressions sont des expressions faciales brèves et subtiles qui apparaissent et disparaissent en une fraction de seconde. Ce type d'expressions reflèterait "l'intention réelle" de l'être humain. Elles ont été étudiées pour mieux comprendre les communications non verbales et dans un contexte médicale lorsqu'il devient presque impossible d'engager une conversation ou d'essayer de traduire les émotions du visage ou le langage corporel d'un patient. Cependant, détecter et reconnaître les micro-expressions est une tâche difficile pour l'homme. Il peut donc être pertinent de développer de
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Chavali, Gautam Krishna, Sai Kumar N. V. Bhavaraju, Tushal Adusumilli, and VenuGopal Puripanda. "Micro-Expression Extraction For Lie Detection Using Eulerian Video (Motion and Color) Magnication." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3467.

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Lie-detection has been an evergreen and evolving subject. Polygraph techniques have been the most popular and successful technique till date. The main drawback of the polygraph is that good results cannot be attained without maintaining a physical contact, of the subject under test. In general, this physical contact would induce extra consciousness in the subject. Also, any sort of arousal in the subject triggers false positives while performing the traditional polygraph based tests. With all these drawbacks in the polygraph, also, due to rapid developments in the fields of computer vision and
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Lin, Min-Chin, and 林敏勤. "The Development and Application of Facial Micro-Expression Recognition System." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/46498631241766890755.

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碩士<br>國立中興大學<br>資訊管理學系所<br>104<br>Effective teaching feedback can help teachers acquire student learning conditions, but also revise their teaching schedule and improve their teaching practices. Apart from the students'' exam score, students’ facial expression is an important clue for teaching feedback. If we can get students'' academic emotions through their facial expression, it can help teachers acquire more information about student learning conditions. However, the students’ facial expression is likely to control or conceal, teachers do not recognize it. Therefore, this study captured the
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Adegun, Iyanu Pelumi. "Automatic recognition of micro-expressions using local binary patterns on three orthogonal planes and extreme learning machine." Thesis, 2017. https://hdl.handle.net/10539/24181.

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A Dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Master of Science. September 2017<br>Recognition of micro-expressions is a growing research area as a result of its application in revealing subtle intention of humans especially under high stake situations. Owing to micro-expressions' short duration and low inten- sity, e orts to train humans in their recognition has resulted in very low performance. The use of temporal methods (on image sequences) and static methods (on apex frames) wer
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Books on the topic "Micro Expression Recognition"

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Geri, Guillaume, and Jean-Paul Mira. Host–pathogen interactions in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0306.

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Infection by a pathogenic micro-organism triggers a coordinated activation of both innate and adaptive immune responses. The innate immune response quickly triggers an antimicrobial response that will initiate development of a pathogen-specific, long-lasting adaptive immune response. Accurate recognition of microbial-associated molecular patterns by pattern-recognition receptors (PRRs) is the cornerstone of this immediate response. Most studied PRRs are Toll-like receptors (TLRs) and their kinase signalling cascades that activate nuclear transcription factors, and induce gene expression and cy
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Book chapters on the topic "Micro Expression Recognition"

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Zhang, Mingyue, Zhaoxin Huan, and Lin Shang. "Micro-Expression Recognition Using Micro-Variation Boosted Heat Areas." In Pattern Recognition and Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60639-8_44.

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Praneeth, Nasaka Ravi, Godavarthi Sri Sai Vikas, Ravuri Naveen Kumar, and T. Anuradha. "Facial Micro-expression Recognition Using Deep Learning." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4863-3_44.

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Wang, Shiqi, Fei Long, and Junfeng Yao. "Micro-expression Recognition Based on PCB-PCANet+." In Neural Information Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8076-5_13.

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Xie, Zhihua, Sijia Cheng, Xiaoyu Liu, and Jiawei Fan. "Adaptive Enhanced Micro-expression Spotting Network Based on Multi-stage Features Extraction." In Biometric Recognition. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20233-9_29.

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Sumi, Kazuhiko, and Tomomi Ueda. "Micro-Expression Recognition for Detecting Human Emotional Changes." In Human-Computer Interaction. Novel User Experiences. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39513-5_6.

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Zhang, Ming, Yu-Hsin Chen, and Xiaolan Fu. "Suppression of Alpha Oscillation During Micro-expression Recognition." In Computer Vision – ACCV 2016 Workshops. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54407-6_36.

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Malik, Parul, and Jaiteg Singh. "Micro Expression Recognition - Contemporary Challenges, Options and Analysis." In Intelligent Systems Design and Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64850-2_3.

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Ni, Weihang, Shuhuan Zhao, Longfei Yu, and Yanshuang Zhao. "Micro-expression Recognition Based on Multi-scale Attention." In Proceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023). Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0855-0_40.

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Ba, Renwei, Xiao Li, Ruimin Yang, Chunlei Li, and Zhoufeng Liu. "Adaptive and Compact Graph Convolutional Network for Micro-expression Recognition." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8546-3_13.

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Qu, Fangbing, Supei Yan, Jiuqing Liang, and Jianping Wang. "Effect of Short-Term Micro-expression Training on the Micro-expression Recognition Performance of Preschool Children." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7983-3_5.

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Conference papers on the topic "Micro Expression Recognition"

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Qing, Lingfeng, and Fenglin Huang. "Micro-expression Recognition Based on Capsule Network." In 2024 International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC). IEEE, 2024. https://doi.org/10.1109/csis-iac63491.2024.10919257.

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Zhang, Liyan, Jiayan Li, Shuang Chen, Jiaxin Du, and Zhongyue Zheng. "An improved HTNet micro-expression recognition method." In 2024 International Conference on Advanced Control Systems and Automation Technologies (ACSAT). IEEE, 2024. https://doi.org/10.1109/acsat63853.2024.10823960.

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Unnisa, Mahera, and V. Ganesan. "Micro Expression Recognition Using ML Based XGBoost Classifier." In 2024 2nd World Conference on Communication & Computing (WCONF). IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692178.

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Li, Daxiang, Nannan Qiao, and Ying Liu. "Micro-Expression Recognition Based on Temporal Sensitive Transformer." In 2024 6th International Conference on Natural Language Processing (ICNLP). IEEE, 2024. http://dx.doi.org/10.1109/icnlp60986.2024.10692680.

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Xiao, Peilin, Yueyi Zhang, Dachun Kai, Yansong Peng, Zheyu Zhang, and Xiaoyan Sun. "A Micro-Expression Recognition System with Event Cameras." In 2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2024. http://dx.doi.org/10.1109/icmew63481.2024.10645394.

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Kong, Xiangfei, Guangyuan Liu, Zhengjiao Wan, and Yahao Sun. "T-SMCFN: An EEG-based Framework for Macro-expression, Micro-expression, and No-expression Recognition." In 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). IEEE, 2025. https://doi.org/10.1109/ainit65432.2025.11035256.

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He, Yuhong, Yueqi Xiong, Lin Ma, and Haifeng Li. "Multi-source Unsupervised Domain Adaptation for Micro-expression Recognition." In 2024 IEEE 17th International Conference on Signal Processing (ICSP). IEEE, 2024. https://doi.org/10.1109/icsp62129.2024.10846342.

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Zhou, Mengxuan. "Psychological activity data classification based on micro-expression recognition." In 2024 Second International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2024. http://dx.doi.org/10.1109/icici62254.2024.00111.

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Fang, Chun-Ting, Tsung-Jung Liu, and Kuan-Hsien Liu. "Enhanced Feature Extraction and Attention Mechanisms for Micro-Expression Recognition." In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2024. http://dx.doi.org/10.1109/mlsp58920.2024.10734789.

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Li, Bokai, Wangwang Xu, Houying Qin, Lei Jin, Jing Wang, and Zeliang Zhao. "Micro-Expression Spotting and Recognition Network based on Apex Frame." In 2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML). IEEE, 2024. https://doi.org/10.1109/prml62565.2024.10779771.

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Reports on the topic "Micro Expression Recognition"

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Li, Wen, and Chen Chen. The application of micro-expression recognition technique in psychiatry research: A systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.12.0122.

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Eshed, Yuval, and Sarah Hake. Exploring General and Specific Regulators of Phase Transitions for Crop Improvement. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7699851.bard.

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The transition of plants from a juvenile to adult growth phase entails a wide range of changes in growth habit, physiological competence and composition. Strikingly, most of these changes are coordinated by the expression of a single regulator, micro RNA 156 (miR156) that coordinately regulates a family of SBP genes containing a miR156 recognition site in the coding region or in their 3’ UTR. In the framework of this research, we have taken a broad taxonomic approach to examine the role of miR156 and other genetic regulators in phase change transition and its implication to plant development a
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Casimiro, June Ann, Karenina Romualdo, and Via Shane Santiago. Innovation Strategies in Traditional Cultural Expressions MSMEs in the Philippines: A Case Study. Philippine Institute for Development Studies, 2025. https://doi.org/10.62986/dp2024.48.

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This study investigates the innovation strategies of cultural micro, small, and medium enterprises (MSMEs) in Northern and Central Luzon, Philippines, operating within the domain of traditional cultural expressions, particularly traditional crafts and culinary crafts. These enterprises play a dual role in preserving cultural heritage and contributing to local economic development. As cultural and creative industries (CCIs) gain increasing recognition for their economic and social contributions, cultural MSMEs in developing regions face unique challenges. Through a qualitative case study approa
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