Facial Expression Recognition (FER)

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.

( Image credit: DeXpression )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 51–75 of 492 papers

Title	Date	Tasks	Status	Hype	Score
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition	Oct 25, 2023	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	1	5
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation	Nov 24, 2022	Contrastive LearningData Augmentation	CodeCode Available	1	5
POSTER++: A simpler and stronger facial expression recognition network	Jan 28, 2023	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	1	5
A Survey on Facial Expression Recognition of Static and Dynamic Emotions	Aug 28, 2024	cross-modal alignmentFacial Expression Recognition	CodeCode Available	1	5
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition	Jun 7, 2021	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	1	5
ExpLLM: Towards Chain of Thought for Facial Expression Recognition	Sep 4, 2024	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	1	5
Explore Image Deblurring via Blur Kernel Space	Apr 1, 2021	Blind Image DeblurringDeblurring	CodeCode Available	1	5
AU-Expression Knowledge Constrained Representation Learning for Facial Expression Recognition	Dec 29, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	1	5
Face2Exp: Combating Data Biases for Facial Expression Recognition	Jan 1, 2022	Face RecognitionFacial Expression Recognition	CodeCode Available	1	5
Facial Emotion Recognition Using Transfer Learning in the Deep CNN	Apr 27, 2021	Emotion RecognitionFacial Emotion Recognition	CodeCode Available	1	5
Facial Expression Recognition using Residual Masking Network	May 5, 2021	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	1	5
Relative Uncertainty Learning for Facial Expression Recognition	Dec 1, 2021	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	1	5
Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues	Feb 1, 2024	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	1	5
Learning Facial Representations from the Cycle-consistency of Face	Aug 7, 2021	Face ReconstructionFacial Expression Recognition	CodeCode Available	1	5
Challenges in Representation Learning: A report on three machine learning contests	Jul 1, 2013	BIG-bench Machine LearningFacial Expression Recognition	CodeCode Available	1	5
Facial Expression Recognition with Deep Learning	Apr 8, 2020	Deep LearningFacial Expression Recognition	CodeCode Available	1	5
Norface: Improving Facial Expression Analysis by Identity Normalization	Jul 22, 2024	ClassificationEmotion Recognition	CodeCode Available	1	5
Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion From a Single-View Image	Jan 5, 2022	Face GenerationFacial Expression Recognition	CodeCode Available	1	5
Compacting, Picking and Growing for Unforgetting Continual Learning	Oct 15, 2019	Age And Gender ClassificationContinual Learning	CodeCode Available	1	5
From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos	Dec 9, 2023	Dynamic Facial Expression RecognitionFacial Expression Recognition	CodeCode Available	1	5
A Dual-Branch Adaptive Distribution Fusion Framework for Real-World Facial Expression Recognition	May 5, 2023	Facial Expression Recognition (FER)	CodeCode Available	1	5
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning	Jun 10, 2019	Age And Gender ClassificationContinual Learning	CodeCode Available	1	5
BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient	Apr 18, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	1	5
Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild	Aug 19, 2022	Dynamic Facial Expression RecognitionFacial Expression Recognition	CodeCode Available	1	5
Suppressing Uncertainties for Large-Scale Facial Expression Recognition	Feb 24, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	1	5

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All datasets AffectNet RAF-DB FER2013 FER+Acted Facial Expressions In The Wild (AFEW)CK+FERPlus JAFFE SFEW Aff-Wild2 BP4D DISFA

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResEmoteNet	Accuracy (7 emotion)	72.93	—	Unverified
2	Norface	Accuracy (8 emotion)	68.69	—	Unverified
3	EmoAffectNet	Accuracy (7 emotion)	66.49	—	Unverified
4	Emotion-GCN	Accuracy (7 emotion)	66.46	—	Unverified
5	FaceBehaviorNet	Accuracy (7 emotion)	65.4	—	Unverified
6	Ada-DF	Accuracy (7 emotion)	65.34	—	Unverified
7	EAC	Accuracy (7 emotion)	65.32	—	Unverified
8	PAENet	Accuracy (7 emotion)	65.29	—	Unverified
9	DACL	Accuracy (7 emotion)	65.2	—	Unverified
10	DDAMFN++	Accuracy (8 emotion)	65.04	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResEmoteNet	Overall Accuracy	94.76	—	Unverified
2	FMAE	Overall Accuracy	93.45	—	Unverified
3	QCS	Overall Accuracy	93.02	—	Unverified
4	Norface	Overall Accuracy	92.97	—	Unverified
5	S2D	Overall Accuracy	92.57	—	Unverified
6	BTN	Overall Accuracy	92.54	—	Unverified
7	GReFEL	Overall Accuracy	92.47	—	Unverified
8	DDAMFN++	Overall Accuracy	92.34	—	Unverified
9	DCJT	Overall Accuracy	92.24	—	Unverified
10	POSTER++	Overall Accuracy	92.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EfficientFER	Accuracy	82.47	—	Unverified
2	FERNeXt-SDAFE	Accuracy	81.33	—	Unverified
3	ResEmoteNet	Accuracy	79.79	—	Unverified
4	Ensemble ResMaskingNet with 6 other CNNs	Accuracy	76.82	—	Unverified
5	Mini-ResEmoteNet (A)	Accuracy	76.33	—	Unverified
6	EmoNeXt	Accuracy	76.12	—	Unverified
7	Segmentation VGG-19	Accuracy	75.97	—	Unverified
8	Local Learning Deep+BOW	Accuracy	75.42	—	Unverified
9	LHC-Net	Accuracy	74.42	—	Unverified
10	Residual Masking Network	Accuracy	74.14	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PAtt-Lite	Accuracy	95.55	—	Unverified
2	GReFEL	Accuracy	93.09	—	Unverified
3	QCS	Accuracy	91.85	—	Unverified
4	ResNet18 Dense Architecture	Accuracy	91.41	—	Unverified
5	DDAMFN	Accuracy	90.74	—	Unverified
6	KTN	Accuracy	90.49	—	Unverified
7	Vit-base + MAE	Accuracy	90.18	—	Unverified
8	FER-VT	Accuracy	90.04	—	Unverified
9	EAC	Accuracy	89.64	—	Unverified
10	LResNet50E-IR	Accuracy	89.26	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet50	Accuracy(on validation set)	65.5	—	Unverified
2	LResNet50E-IR (5 models with augmentation)	Accuracy(on validation set)	65.5	—	Unverified
3	EAC	Accuracy(on validation set)	65.32	—	Unverified
4	LResNet50E-IR (1 model with augmentation)	Accuracy(on validation set)	63.7	—	Unverified
5	LResNet50E-IR (1 model)	Accuracy(on validation set)	61.1	—	Unverified
6	Multi-task EfficientNet-B0	Accuracy(on validation set)	59.27	—	Unverified
7	resnet18_noisy	Accuracy(on validation set)	55.17	—	Unverified
8	resnet18	Accuracy(on validation set)	51.18	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EmoNeXt	Accuracy (8 emotion)	100	—	Unverified
2	PAtt-Lite	Accuracy (7 emotion)	100	—	Unverified
3	ViT + SE	Accuracy (7 emotion)	99.8	—	Unverified
4	FAN	Accuracy (7 emotion)	99.7	—	Unverified
5	Nonlinear eval on SL + SSL puzzling (B0)	Accuracy (7 emotion)	98.23	—	Unverified
6	DeepEmotion	Accuracy (7 emotion)	98	—	Unverified
7	FN2EN	Accuracy (8 emotion)	96.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	KTN	Accuracy(pretrained)	90.49	—	Unverified
2	RAN (VGG-16)	Accuracy(pretrained)	89.16	—	Unverified
3	SENet Teacher	Accuracy(pretrained)	88.88	—	Unverified
4	Local Learning Deep + BOW	Accuracy(pretrained)	87.76	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TL	Accuracy	99.52	—	Unverified
2	GReFEL	Accuracy	96.67	—	Unverified
3	ViT	Accuracy	94.83	—	Unverified
4	DeepEmotion	Accuracy	92.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Ada-DF	Accuracy	60.46	—	Unverified
2	RAN (VGG16+ResNet18)	Accuracy	56.4	—	Unverified
3	ViT + SE	Accuracy	54.29	—	Unverified
4	Island Loss	Accuracy	52.52	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GReFEL	Accuracy	72.48	—	Unverified
2	EmoAffectNet LSTM	UAR	52.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Norface	ICC	0.74	—	Unverified
2	Ours (VGG-F)	ICC	0.72	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Norface	ICC	0.67	—	Unverified
2	Ours (VGG-F)	ICC	0.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeepEmotion	Accuracy	99.3	—	Unverified
2	GReFEL	Accuracy	98.18	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeXpression	Accuracy	98.63	—	Unverified
2	Facial Motion Prior Network	Accuracy	82.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Dynamic MTL	Accuracy (10-fold)	89.6	—	Unverified
2	PPDN	Accuracy (10-fold)	84.59	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Covariance Pooling	Accuracy	87	—	Unverified
2	Multi Label Output	Accuracy	79.26	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Covariance Pooling	Accuracy	58.14	—	Unverified
2	VGG-VD-16	Accuracy	54.82	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EfficientFace	Accuracy	85.87	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Sequential forward selection	Accuracy	88.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EmoAffectNet LSTM	UAR	79	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResEmoteNet	Accuracy	75.67	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ViT + SE	Accuracy	87.22	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EmoAffectNet LSTM	UAR	69.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EmoAffectNet LSTM	UAR	82.8	—	Unverified