Micro-Expression Recognition
15 papers with code • 1 benchmarks • 1 datasets
Facial Micro-Expression Recognition is a challenging task in identifying suppressed emotion in a high-stake environment, often comes in very brief duration and subtle changes.
CASME II
Latest papers with no code
Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning
Micro-expressions have the characteristics of short duration and low intensity, and it is difficult to train a high-performance classifier with the limited number of existing micro-expressions.
Geometric Graph Representation with Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition
However, the discriminability of facial landmarks for MER is unclear.
Transferring Dual Stochastic Graph Convolutional Network for Facial Micro-expression Recognition
To improve the recognition performance of the small micro-expression data, this paper presents a transferring dual stochastic Graph Convolutional Network (TDSGCN) model.
Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition
Being spontaneous, micro-expressions are useful in the inference of a person's true emotions even if an attempt is made to conceal them.
Micro-Expression Recognition Using Histogram of Image Gradient Orientation on Diagonal Planes
Micro-expression conveys a person's state-of-mind; therefore, it can be helpful in many cases.
Region attention and graph embedding network for occlusion objective class-based micro-expression recognition
More specifically, the backbone network aims at extracting feature representations from different facial regions, RI module computing an adaptive weight from the region itself based on attention mechanism with respect to the unobstructedness and importance for suppressing the influence of occlusion, and RR module exploiting the progressive interactions among these regions by performing graph convolutions.
Deep Learning for Micro-expression Recognition: A Survey
In this survey, we provide a comprehensive review of deep micro-expression recognition (MER), including datasets, deep MER pipeline, and the bench-marking of most influential methods.
Self-Supervised Approach for Facial Movement Based Optical Flow
The aim of this work is threefold: (1) exploring self-supervised techniques to generate optical flow ground truth for face images; (2) computing baseline results on the effects of using face data to train Convolutional Neural Networks (CNN) for predicting optical flow; and (3) using the learned optical flow in micro-expression recognition to demonstrate its effectiveness.
A comparative study on movement feature in different directions for micro-expression recognition
In this paper, HSDG in every direction is concatenated with LBP-TOP to obtain the LBP with Single Direction Gradient (LBP-SDG) and analyze which direction of movement feature is more discriminative for micro-expression recognition.
Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition
It aims to obtain salient and discriminative features for specific expressions and also predict expression by fusing the expression-specific features.
