Emotion Recognition

Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1371–1380 of 2041 papers

Title	Date	Tasks	Status	Hype
Expression Recognition Analysis in the Wild	Jan 22, 2021	Emotion RecognitionFacial Expression Recognition	—Unverified	0
Analysis of Basic Emotions in Texts Based on BERT Vector Representation	Jan 21, 2021	Emotion Recognition	—Unverified	0
4D Attention-based Neural Network for EEG Emotion Recognition	Jan 14, 2021	Brain Computer InterfaceEEG	—Unverified	0
Fast Facial Landmark Detection and Applications: A Survey	Jan 12, 2021	Emotion RecognitionFace Reenactment	—Unverified	0
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification	Jan 11, 2021	Causal Emotion EntailmentCausal Inference	CodeCode Available	0
Combining deep and unsupervised features for multilingual speech emotion recognition	Jan 10, 2021	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	0
Transformer-based approach towards music emotion recognition from lyrics	Jan 6, 2021	Emotion RecognitionInformation Retrieval	CodeCode Available	1
Facial Expressions Recognition System Using FPGA-Based Convolutional Neural Network	Jan 5, 2021	Emotion Recognition	CodeCode Available	0
Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion Recognition	Jan 5, 2021	Emotion RecognitionFew-Shot Learning	CodeCode Available	0
A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition	Jan 4, 2021	Cross-corpusDeep Reinforcement Learning	CodeCode Available	0

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All datasets Emomusic RAVDESS EMOTIC FER2013 MaSaC_ERC MPED MSP-Podcast SEED คลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	M2D-CLAP	EmoA	77.4	—	Unverified
2	M2D2	EmoA	76.7	—	Unverified
3	M2D	EmoA	76.1	—	Unverified
4	Jukebox (Pre-training: CALM)	EmoA	72.1	—	Unverified
5	CLMR (Pre-training: contrastive)	EmoA	67.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+Attention	Accuracy	86.7	—	Unverified
2	MultiMAE-DER	WAR	83.61	—	Unverified
3	Intermediate-Attention-Fusion	Accuracy	81.58	—	Unverified
4	Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedST	Accuracy	80.08	—	Unverified
5	ERANN-0-4	Accuracy	74.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CAGE	Top-3 Accuracy (%)	14.73	—	Unverified
2	FocusCLIP	Top-3 Accuracy (%)	13.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	VGG based	5-class test accuracy	66.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MaSaC-ERC-Z	F1-score (Weighted)	51.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BiHDM	Accuracy	40.34	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	w2v2-L-robust-12	Concordance correlation coefficient (CCC)	0.64	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	4D-aNN	Accuracy	96.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	1'"	1	—	Unverified