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 426–450 of 492 papers

Title	Date	Tasks	Status
A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition	Oct 1, 2017	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Constrained Deep Transfer Feature Learning and its Applications	Sep 23, 2017	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Is human face processing a feature- or pattern-based task? Evidence using a unified computational method driven by eye movements	Sep 4, 2017	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Facial Expression Recognition using Visual Saliency and Deep Learning	Aug 26, 2017	Deep LearningFacial Expression Recognition	—Unverified
Objective Classes for Micro-Facial Expression Recognition	Aug 24, 2017	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild	Aug 14, 2017	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN	Jul 31, 2017	ClassificationFacial Expression Recognition	—Unverified
A Novel Space-Time Representation on the Positive Semidefinite Con for Facial Expression Recognition	Jul 20, 2017	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks	May 22, 2017	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Local Shape Spectrum Analysis for 3D Facial Expression Recognition	May 19, 2017	3D Facial Expression Recognition3D geometry	—Unverified
Convolutional Neural Networks for Facial Expression Recognition	Apr 22, 2017	Facial Expression Recognition (FER)GPU	CodeCode Available
Deep generative-contrastive networks for facial expression recognition	Mar 21, 2017	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields	Mar 20, 2017	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data	Mar 11, 2017	Emotion RecognitionFacial Expression Recognition	CodeCode Available
FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge	Feb 14, 2017	BenchmarkingFacial Action Unit Detection	—Unverified
Side Information in Robust Principal Component Analysis: Algorithms and Applications	Feb 2, 2017	DenoisingFacial Expression Recognition	—Unverified
Greedy Search for Descriptive Spatial Face Features	Jan 7, 2017	DescriptiveFacial Expression Recognition	CodeCode Available
Baseline CNN structure analysis for facial expression recognition	Nov 14, 2016	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Learning Grimaces by Watching TV	Oct 7, 2016	Emotion RecognitionFace Verification	—Unverified
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition	Sep 21, 2016	Face RecognitionFacial Expression Recognition (FER)	—Unverified
From Facial Expression Recognition to Interpersonal Relation Prediction	Sep 21, 2016	AttributeFacial Expression Recognition	—Unverified
SenTion: A framework for Sensing Facial Expressions	Aug 16, 2016	CPUFacial Expression Recognition	—Unverified
Facial Expression Recognition Using a Hybrid CNN-SIFT Aggregator	Aug 9, 2016	Face RecognitionFacial Expression Recognition	—Unverified
A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset	Aug 4, 2016	Dataset GenerationFacial Expression Recognition	—Unverified
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution	Aug 3, 2016	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available

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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