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computer-vision-6

Micro-Expression Recognition

3260 papers • 126 benchmarks • 313 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.

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Benchmarks

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Trend

Dataset

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CASME II

CASME II

Libraries

i

Use these libraries to find micro-expression-recognition-6 models and implementations

Datasets

CASME II

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Most implemented papers

Enriched Long-Term Recurrent Convolutional Network for Facial Micro-Expression Recognition

Weiyao Lin, John See, Huai-Qian Khor, R. Phan•Mon May 14 2018

An Enriched Long-term Recurrent Convolutional Network (ELRCN) that first encodes each micro- expression frame into a feature vector through CNN module(s), then predicts the micro-expression by passing the feature vectorthrough a Long Short-term Memory (LSTM) module.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

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Dual-stream Shallow Networks for Facial Micro-expression Recognition

Weiyao Lin, John See, Huai-Qian Khor, R. Phan, Sze‐Teng Liong•Sat Aug 31 2019

Micro-expressions are spontaneous, brief and subtle facial muscle movements that exposes underlying emotions. Motivated by recent exploits into deep learning for micro-expression analysis, we propose a lightweight dual-stream shallow network in the form of a pair of truncated CNNs with heterogeneous input features. The merging of the convolutional features allows for discriminative learning of micro-expression classes stemming from both streams. Using activation heatmaps, we further demonstrate that salient facial areas are well emphasized, and correspond closely to relevant action units belonging to emotion classes. We empirically validate the proposed network on three benchmark databases, obtaining state-of-the-art performance on the CASME II and SAMM while remaining competitive on the SMIC. Further observations point towards the sufficiency of utilizing shallower deep networks for micro-expression recognition.

148 0

GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

Jiaqi Tang, Xuesong Niu, Hao Lu, Jiyao Wang, Yin Wang, Qingyong Hu, Yuting Zhang, Kaishen Yuan, Bin Huang, Zitong Yu, Dengbo He, Shuiguang Deng, Hao Chen, Yingcong Chen, Shiguang Shan•Fri Mar 08 2024

The results show that GPT-4V has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate, and provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing.

28 0

Micro-Attention for Micro-Expression recognition

Min Peng, Chongyang Wang, Tao Bi, Tong Chen•Mon Nov 05 2018

This work proposes a novel attention mechanism called micro-attention cooperating with residual network that enables the network to learn to focus on facial areas of interest covering different action units and pushes the boundary of automatic recognition of micro-expression.

151 0

Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition

John See, Huai-Qian Khor, Sze‐Teng Liong, Y. Gan•Sat Feb 09 2019

A Shallow Triple Stream Three-dimensional CNN (STSTNet) that is computationally light whilst capable of extracting discriminative high level features and details of micro-expressions is designed.

231 0

Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks

S. Dubey, Snehasis Mukherjee, S. Reddy, Surya Teja Karri•Tue Mar 26 2019

Two 3D-CNN methods are proposed: MicroExpSTCNN and MicroExpFuseNet, for spontaneous facial micro-expression recognition by exploiting the spatiotemporal information in CNN framework, which outperforms the state-of-the-art methods.

105 0

A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition

Min Peng, Chongyang Wang, Tao Bi, Tong Chen, Xiangdong Zhou, Yu Shi•Sat Apr 06 2019

A novel Apex-Time Network (ATNet) is proposed to recognize micro-expression based on spatial information from the apex frame as well as on temporalInformation from the respective-adjacent frames so that the model with such temporal information is more robust in cross-dataset validations.

62 0

ICE-GAN: Identity-Aware and Capsule-Enhanced GAN with Graph-Based Reasoning for Micro-Expression Recognition and Synthesis

Weidong (Tom) Cai, Jianhui Yu, Chaoyi Zhang, Yang Song•Sat Jul 17 2021

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. The short detection window, the subtle facial muscle movements, and the limited training samples make micro-expression recognition challenging. To this end, we propose a novel Identity-aware and Capsule-Enhanced Generative Adversarial Network with graph-based reasoning (ICE-GAN), introducing micro-expression synthesis as an auxiliary task to assist recognition. The generator produces synthetic faces with controllable micro-expressions and identity-aware features, whose long-ranged dependencies are captured through the graph reasoning module (GRM), and the discriminator detects the image authenticity and expression classes. Our ICE-GAN was evaluated on Micro-Expression Grand Challenge 2019 (MEGC2019) with a significant improvement (12.9%) over the winner and surpassed other state-of-the-art methods.

17 0

Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition

Yuan Zong, Wenming Zheng, Xingxun Jiang•Mon Nov 29 2021

Experimental results show that the proposed TGSR learns the discriminative and explicable regions, and outperforms most state-of-the-art subspace-learning-based domain-adaptive methods for CDMER.

8 0

How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?

Liang Zheng, Tom Gedeon, Zhongdao Wang, Yuchi Liu•Thu Dec 02 2021

A protocol to automatically synthesize large scale MiE training data that allow us to train improved recognition models for real-world test data and discover three types of Action Units (AUs) that can constitute trainable MiEs.

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Micro-Expression Recognition | State-of-the-Art