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 1561–1570 of 2041 papers

Title	Date	Tasks	Status
Annotated Corpus of Tweets in English from Various Domains for Emotion Detection	Dec 1, 2020	Emotion Recognition	—Unverified
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus	Sep 1, 2017	Emotion RecognitionGeneral Classification	—Unverified
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets	Aug 8, 2018	Emotion RecognitionModel Selection	—Unverified
A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion	Feb 12, 2025	Contrastive LearningEmotion Recognition	—Unverified
A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition	May 11, 2019	EEGEEG Emotion Recognition	—Unverified
A Novel Dialect-Aware Framework for the Classification of Arabic Dialects and Emotions	Feb 13, 2025	Emotion ClassificationEmotion Recognition	—Unverified
A Novel Enhanced Convolution Neural Network with Extreme Learning Machine: Facial Emotional Recognition in Psychology Practices	Aug 5, 2022	Emotion RecognitionFacial Emotion Recognition	—Unverified
A Novel Exploitative and Explorative GWO-SVM Algorithm for Smart Emotion Recognition	Jan 5, 2023	Emotion Recognition	—Unverified
A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding	Jun 29, 2018	Action RecognitionEmotion Recognition	—Unverified
A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition	Jan 15, 2022	Emotion RecognitionLanguage Modeling	—Unverified

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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