scholarly journals Micro-Expression Recognition Enhanced by Macro-Expression from Spatial-Temporal Domain

2021 ◽  
Author(s):  
Bin Xia ◽  
Shangfei Wang
Keyword(s):  
High Performance ◽  
Spatial Domain ◽  
Behavior Patterns ◽  
Expression Recognition ◽  
Expression Data ◽  
Baseline Model ◽  
Temporal Domain ◽  
Temporal Features ◽  
Facial Behavior ◽  
Micro Expression

Facial micro-expression recognition has attracted much attention due to its objectiveness to reveal the true emotion of a person. However, the limited micro-expression datasets have posed a great challenge to train a high performance micro-expression classifier. Since micro-expression and macro-expression share some similarities in both spatial and temporal facial behavior patterns, we propose a macro-to-micro transformation framework for micro-expression recognition. Specifically, we first pretrain two-stream baseline model from micro-expression data and macro-expression data respectively, named MiNet and MaNet. Then, we introduce two auxiliary tasks to align the spatial and temporal features learned from micro-expression data and macro-expression data. In spatial domain, we introduce a domain discriminator to align the features of MiNet and MaNet. In temporal domain, we introduce relation classifier to predict the correct relation for temporal features from MaNet and MiNet. Finally, we propose contrastive loss to encourage the MiNet to give closely aligned features to all entries from the same class in each instance. Experiments on three benchmark databases demonstrate the superiority of the proposed method.

scholarly journals Evaluation of the Spatio-Temporal Features and GAN for Micro-Expression Recognition System

2020 ◽  
Vol 92 (7) ◽  
pp. 705-725
Author(s):  
Sze-Teng Liong ◽  
Y. S. Gan ◽  
Danna Zheng ◽  
Shu-Meng Li ◽  
Hao-Xuan Xu ◽  
...  
Keyword(s):  
Recognition System ◽  
Expression Recognition ◽  
Temporal Features ◽  
Spatio Temporal ◽  
Micro Expression

scholarly journals Explicit control of step timing during split-belt walking reveals interdependent recalibration of movements in space and time

2019 ◽  
Author(s):  
Marcela Gonzalez-Rubio ◽  
Nicolas F. Velasquez ◽  
Gelsy Torres-Oviedo
Keyword(s):  
Motor Adaptation ◽  
Spatial Domain ◽  
Hierarchical Organization ◽  
The Other ◽  
Spatial Control ◽  
Limb Position ◽  
Locomotor Pattern ◽  
Temporal Domain ◽  
Temporal Features ◽  
Spatio Temporal

ABSTRACTSplit-belt treadmills that move the legs at different speeds are thought to update internal representations of the environment, such that this novel condition generates a new locomotor pattern with distinct spatio-temporal features compared to those of regular walking. It is unclear the degree to which such recalibration of movements in the spatial and temporal domains is interdependent. In this study, we explicitly altered subjects’ limb motion in either space or time during split-belt walking to determine its impact on the adaptation of the other domain. Interestingly, we observed that motor adaptation in the spatial domain was susceptible to altering the temporal domain, whereas motor adaptation in the temporal domain was resilient to modifying the spatial domain. This nonreciprocal relation suggests a hierarchical organization such that the control of timing in locomotion has an effect on the control of limb position. This is of translational interest because clinical populations often have a greater deficit in one domain compared to the other. Our results suggest that explicit changes to temporal deficits cannot occur without modifying the spatial control of the limb.

Aspect and space

2017 ◽  
Author(s):  
Lucas Champollion
Keyword(s):  
Spatial Domain ◽  
The Other ◽  
Alternative View ◽  
Temporal Domain ◽  
Spatial Aspect ◽  
Temporal Aspect ◽  
The Way

This chapter models the relation between temporal aspect (run for an hour vs. *run all the way to the store for an hour) and spatial aspect (meander for a mile vs. *end for a mile) previously discussed by Gawron (2009). The chapter shows that for-adverbials impose analogous conditions on the spatial domain and on the temporal domain, and that an event may satisfy stratified reference with respect to one of the domains without satisfying it with respect to the other one as well. This provides the means to extend the telic-atelic opposition to the spatial domain. The chapter argues in some detail that stratified reference is in this respect empirically superior to an alternative view of telicity based on divisive reference (Krifka 1998).

scholarly journals Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2003 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Shihao Ye ◽  
Liang Zhao ◽  
Zhicheng Dai
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatial Features ◽  
Face Images ◽  
Temporal Features ◽  
The Face ◽  
In The Wild ◽  
Fusion Features

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

Micro-Expression Recognition using 3D - CNN

2020 ◽  
pp. 5-13
Author(s):  
Vishal Dubey ◽  
◽  
◽  
◽  
Bhavya Takkar ◽  
...  
Keyword(s):  
Emotional Response ◽  
High Speed ◽  
Actual State ◽  
Time Interval ◽  
Short Time Interval ◽  
Expression Recognition ◽  
Specific Emotion ◽  
3D Cnn ◽  
Short Time ◽  
Micro Expression

Micro-expression comes under nonverbal communication, and for a matter of fact, it appears for minute fractions of a second. One cannot control micro-expression as it tells about our actual state emotionally, even if we try to hide or conceal our genuine emotions. As we know that micro-expressions are very rapid due to which it becomes challenging for any human being to detect it with bare eyes. This subtle-expression is spontaneous, and involuntary gives the emotional response. It happens when a person wants to conceal the specific emotion, but the brain is reacting appropriately to what that person is feeling then. Due to which the person displays their true feelings very briefly and later tries to make a false emotional response. Human emotions tend to last about 0.5 - 4.0 seconds, whereas micro-expression can last less than 1/2 of a second. On comparing micro-expression with regular facial expressions, it is found that for micro-expression, it is complicated to hide responses of a particular situation. Micro-expressions cannot be controlled because of the short time interval, but with a high-speed camera, we can capture one's expressions and replay them at a slow speed. Over the last ten years, researchers from all over the globe are researching automatic micro-expression recognition in the fields of computer science, security, psychology, and many more. The objective of this paper is to provide insight regarding micro-expression analysis using 3D CNN. A lot of datasets of micro-expression have been released in the last decade, we have performed this experiment on SMIC micro-expression dataset and compared the results after applying two different activation functions.

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