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

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Showing 301–325 of 492 papers

Title	Date	Tasks	Status
Deep Convolutional Neural Network Based Facial Expression Recognition in the Wild	Oct 3, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available
Deep Evolution for Facial Emotion Recognition	Sep 29, 2020	Emotion RecognitionEvolutionary Algorithms	—Unverified
Micro-Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm	Sep 29, 2020	Emotion RecognitionFacial Expression Recognition	—Unverified
Kernelized dense layers for facial expression recognition	Sep 22, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images	Sep 19, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition	Sep 15, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm	Sep 12, 2020	Face DetectionFacial Expression Recognition	—Unverified
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition	Aug 26, 2020	Adversarial AttackData Augmentation	—Unverified
E-FCNN for tiny facial expression recognition	Aug 20, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild	Aug 13, 2020	Dynamic Facial Expression RecognitionFacial Expression Recognition	—Unverified
Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learning	Aug 12, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available
RAF-AU Database: In-the-Wild Facial Expressions with Subjective Emotion Judgement and Objective AU Annotations	Aug 12, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Noisy Student Training using Body Language Dataset Improves Facial Expression Recognition	Aug 6, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion	Aug 3, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
HEU Emotion: A Large-scale Database for Multi-modal Emotion Recognition in the Wild	Jul 24, 2020	Emotion RecognitionFacial Expression Recognition	—Unverified
Investigating Bias and Fairness in Facial Expression Recognition	Jul 20, 2020	AttributeData Augmentation	—Unverified
Pose-aware Adversarial Domain Adaptation for Personalized Facial Expression Recognition	Jul 12, 2020	DisentanglementDomain Adaptation	—Unverified
Deep Multi-task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing	Jul 9, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
EmotionNet Nano: An Efficient Deep Convolutional Neural Network Design for Real-time Facial Expression Recognition	Jun 29, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available
Meta Transfer Learning for Emotion Recognition	Jun 23, 2020	Emotion RecognitionFacial Expression Recognition	—Unverified
Disentanglement for Discriminative Visual Recognition	Jun 14, 2020	DisentanglementFace Recognition	—Unverified
Facial Expression Recognition using Deep Learning	Jun 7, 2020	Deep LearningFacial Expression Recognition	—Unverified
Label Distribution Learning on Auxiliary Label Space Graphs for Facial Expression Recognition	Jun 1, 2020	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Omni-supervised Facial Expression Recognition via Distilled Data	May 18, 2020	Dataset DistillationFacial Expression Recognition	—Unverified
Convolutional Neural Network for emotion recognition to assist psychiatrists and psychologists during the COVID-19 pandemic: experts opinion	May 15, 2020	Computational EfficiencyEmotion Recognition	—Unverified

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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	PAtt-Lite	Accuracy (7 emotion)	100	—	Unverified
2	EmoNeXt	Accuracy (8 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