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| http://dx.doi.org/10.3389/fpsyg.2018.01672 | DOI Listing |
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MCCA-VNet: A Vit-Based Deep Learning Approach for Micro-Expression Recognition Based on Facial Coding.
Sensors (Basel)
November 2024
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
In terms of facial expressions, micro-expressions are more realistic than macro-expressions and provide more valuable information, which can be widely used in psychological counseling and clinical diagnosis. In the past few years, deep learning methods based on optical flow and Transformer have achieved excellent results in this field, but most of the current algorithms are mainly concentrated on establishing a serialized token through the self-attention model, and they do not take into account the spatial relationship between facial landmarks. For the locality and changes in the micro-facial conditions themselves, we propose the deep learning model MCCA-VNET on the basis of Transformer.
View Article and Find Full Text PDFFacial micro-expression recognition using stochastic graph convolutional network and dual transferred learning.
Neural Netw
October 2024
College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China. Electronic address:
Micro-expression recognition (MER) has drawn increasing attention due to its wide application in lie detection, criminal detection and psychological consultation. However, the best recognition accuracy on recent public dataset is still low compared to the accuracy of macro-expression recognition. In this paper, we propose a novel graph convolution network (GCN) for MER achieving state-of-the-art accuracy.
View Article and Find Full Text PDFS3DCN-OLSR: A shallow 3D CNN method for online learning state recognition.
Heliyon
October 2023
School of Cybersecurity, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China.
The repeated recurrence of COVID-19 has significantly disrupted learning for students in face-to-face instructional settings. While moving from offline to online instruction has proven to be one of the best solutions, classroom management and capturing students' learning states have emerged as important challenges with the increasing popularity of online instruction. To address these challenges, in this paper we propose an online learning status recognition method based on shallow 3D convolution (S3DC-OLSR) for online students, to identify students' online learning states by analysing their micro-expressions.
View Article and Find Full Text PDFFacial Micro-Expression Recognition Enhanced by Score Fusion and a Hybrid Model from Convolutional LSTM and Vision Transformer.
Sensors (Basel)
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MOVEJ Analytics, Fairborn, OH 45324, USA.
In the billions of faces that are shaped by thousands of different cultures and ethnicities, one thing remains universal: the way emotions are expressed. To take the next step in human-machine interactions, a machine (e.g.
View Article and Find Full Text PDFFacial Micro-Expression Recognition Using Double-Stream 3D Convolutional Neural Network with Domain Adaptation.
Sensors (Basel)
March 2023
Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China.
Humans show micro-expressions (MEs) under some circumstances. MEs are a display of emotions that a human wants to conceal. The recognition of MEs has been applied in various fields.
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