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Facial Expression Recognition (FER)

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하위 태스크: 5
평가표: 25
보존된 평가 기록: 167

Facial Expression Recognition (FER) is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face. The goal is to automate the process of determining emotions in real-time, by analyzing the various features of a face such as eyebrows, eyes, mouth, and other features, and mapping them to a set of emotions such as anger, fear, surprise, sadness and happiness.

<span style="color:grey; opacity: 0.6">( Image credit: DeXpression )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://bitbucket.org/aswathyve/unifieddeceptioncorpus

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UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection

https://github.com/1980x/ABAW2020DMACS

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Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information

https://github.com/1980x/ABAW2021DMACS

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Affect Expression Behaviour Analysis in the Wild using Consensual Collaborative Training

https://github.com/1980x/CCT

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Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations

https://github.com/1980x/SCAN-CCI-FER

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Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognition

https://github.com/1980x/dnfer

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Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition

https://github.com/1adrianb/unsupervised-face-representation

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Pre-training strategies and datasets for facial representation learning

https://github.com/4qlaa7/GCF

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GCF: Graph Convolutional Networks for Facial Expression Recognition

https://github.com/ArnabKumarRoy02/ResEmoteNet

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ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition

https://github.com/BR-IDL/PaddleViT

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Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition

https://github.com/Bingwen-Hu/ERGAN-Pytorch

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Unsupervised Eyeglasses Removal in the Wild

https://github.com/ClaudiaShu/SSL-FER

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Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition

https://github.com/CodeShareBot/Micro-Attention-for-Micro-Expression

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Micro-Attention for Micro-Expression recognition

https://github.com/ControlNet/MARLIN

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MARLIN: Masked Autoencoder for facial video Representation LearnINg

https://github.com/CreativePapers/Learn-to-Synthesize-and-Synthesize-to-Learn

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Learn to synthesize and synthesize to learn

https://github.com/FER-LMC/S2D

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From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos

https://github.com/HCPLab-SYSU/AUE-CRL

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AU-Expression Knowledge Constrained Representation Learning for Facial Expression Recognition

https://github.com/HCPLab-SYSU/CD-FER-Benchmark

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Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition

https://github.com/HSE-asavchenko/face-emotion-recognition

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Facial expression and attributes recognition based on multi-task learning of lightweight neural networks

https://github.com/HeyingGithub/Baseline-project-for-MEGC2020_spotting

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Spotting Macro- and Micro-expression Intervals in Long Video Sequences

https://github.com/JDAI-CV/FaceX-Zoo/tree/main/addition_module/DMUE

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Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition

https://github.com/MasoumehSharafi/DSFDA-for-Pain-Assessment

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Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data

https://github.com/Microsoft/FERPlus

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Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution

https://github.com/Mvrjustid/ACII19-Apex-Time-Network

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

https://github.com/OceanSummerDay/HSTA_MER

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Hierarchical Space-Time Attention for Micro-Expression Recognition

https://github.com/Open-Debin/Emotion-FAN

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Frame attention networks for facial expression recognition in videos

https://github.com/PanosAntoniadis/emotion-gcn

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Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition

https://github.com/VinAIResearch/blur-kernel-space-exploring

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Explore Image Deblurring via Blur Kernel Space

https://github.com/ZichenMiao/GNN-with-Low-rank-Learnable-Local-Filters

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Graph Convolution with Low-rank Learnable Local Filters

https://github.com/ZichenMiao/L3Net

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Graph Convolution with Low-rank Learnable Local Filters

https://github.com/ae-1129/lanmsff

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A Lightweight Attention-based Deep Network via Multi-Scale Feature Fusion for Multi-View Facial Expression Recognition

https://github.com/altndrr/clup

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Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition

https://github.com/amilkh/cs230-fer

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Facial Expression Recognition with Deep Learning

https://github.com/arin-upna/biased-heritage

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Biased Heritage: How Datasets Shape Models in Facial Expression Recognition

https://github.com/audeering/w2v2-how-to

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Dawn of the transformer era in speech emotion recognition: closing the valence gap

https://github.com/azadlab/FExGAN

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Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.