Micro-expression spotting method based on human attention mechanism

LI, Jingting; DONG, Zizhao; LIU, Ye; WANG, Su-Jing; ZHUANG, Dongzhe

doi:10.3724/sp.j.1042.2022.02143

Cited by 4 publications

(2 citation statements)

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“…This is essentially a classification problem and does not locate the clips in the video where the micro-expression occurs. Li J et al [21] first introduced the self-supervised learning method into the construction of the micro-expression spotting model. By using auxiliary tasks in a large number of unsupervised videos, a model with temporal and spatial features of micro-expressions was constructed.…”

Section: Related Workmentioning

confidence: 99%

Micro-Expression Spotting Based on VoVNet, Driven by Multi-Scale Features

Yang,

Wu,

2023

Electronics

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Micro-expressions are a type of real emotional expression, which are unconscious and difficult to hide. Identifying these expressions has great potential applications in areas such as civil aviation security, criminal interrogation, and clinical medicine. However, because of their characteristics such as short duration, low intensity, and sparse action units, this makes micro-expression spotting difficult. To address this problem and inspired by object detection methods, we propose a VoVNet-based micro-expression spotting model, driven by multi-scale features. Firstly, VoVNet is used to achieve the extraction and reuse of different scale perceptual field features to improve the feature extraction capability. Secondly, multi-scale features are extracted and fused using the Feature Pyramid Network module, incorporating optical flow features, and by realizing the interactive fusion of fine-grained feature information and semantic feature information. Finally, the model is trained and optimized on CAS(ME)2 and SAMM Long Video. The experimental results show that the F1 score of the proposed model is improved by 0.1963 and 0.2441 on the two datasets compared with the baseline method, which outperforms the most popular spotting methods.

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Section: Related Workmentioning

confidence: 99%

Micro-Expression Spotting Based on VoVNet, Driven by Multi-Scale Features

Yang,

Wu,

2023

Electronics

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“…The dataset preprocessing includes face cropping, frame number normalization, and optical flow sequence extraction. This part mainly analyzes face cropping and optical flow sequence extraction [6].…”

Section: Micro-expression Data Pre-processingmentioning

confidence: 99%

Research on micro-expression recognition method based on attention mechanism

Huang

Feng

et al. 2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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Micro-expressions are rapid, difficult to observe with the naked eye, facial expressions that can reflect real human inner emotions. Micro-expression recognition is still a great challenge due to the characteristics of very short duration and subtle changes (small amplitude of muscle contraction or diastole). Based on this, this paper proposes a 3D convolutional microexpression recognition method based on attention mechanism, which is a dual-stream structure that can effectively utilize the features of image sequence and optical flow sequence. More effective micro-expression features are extracted using Attention layer, Co-Attention layer to better solve the micro-expression recognition task. Adequate experiments are conducted on the dataset to verify that the model has better recognition results.

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