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
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing.
GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition
To bridge this gap, we present the quantitative evaluation results of GPT-4V on 21 benchmark datasets covering 6 tasks: visual sentiment analysis, tweet sentiment analysis, micro-expression recognition, facial emotion recognition, dynamic facial emotion recognition, and multimodal emotion recognition.
HTNet for micro-expression recognition
The transformer layer is used to focus on representing local minor muscle movement with local self-attention in each area.
Micron-BERT: BERT-based Facial Micro-Expression Recognition
uark-cviu/micron-bert
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CVPR 2023
By incorporating these components into an end-to-end deep network, the proposed $\mu$-BERT significantly outperforms all previous work in various micro-expression tasks.
Data Leakage and Evaluation Issues in Micro-Expression Analysis
tvaranka/meb
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Micro-expressions have drawn increasing interest lately due to various potential applications.
MMNet: Muscle motion-guided network for micro-expression recognition
muse1998/mmnet
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By adding the position embeddings of the face generated by PC module at the end of the two branches, the PC module can help to add position information to facial muscle motion pattern features for the MER.
How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?
liuyvchi/mie-x
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To this end, we develop a protocol to automatically synthesize large scale MiE training data that allow us to train improved recognition models for real-world test data.
Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition
To solve these problems, this paper proposes a novel Transfer Group Sparse Regression method, namely TGSR, which aims to 1) optimize the measurement and better alleviate the difference between the source and target databases, and 2) highlight the valid facial regions to enhance extracted features, by the operation of selecting the group features from the raw face feature, where each region is associated with a group of raw face feature, i. e., the salient facial region selection.
ICE-GAN: Identity-aware and Capsule-Enhanced GAN with Graph-based Reasoning for Micro-Expression Recognition and Synthesis
Micro-expressions are reflections of people's true feelings and motives, which attract an increasing number of researchers into the study of automatic facial micro-expression recognition.
Dual-stream shallow networks for facial micro-expression recognition
Justin900429/PyTorch-DSSN-MER
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2019 IEEE International Conference on Image Processing (ICIP) 2019
Micro-expressions are spontaneous, brief and subtle facial muscle movements that exposes underlying emotions.
