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

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Showing 1961–1970 of 2041 papers

Title	Date	Tasks	Status
Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network	May 11, 2023	Emotion Recognition	—Unverified
Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey	Jul 13, 2021	Emotion RecognitionSurvey	—Unverified
Emotion Recognition for In-the-wild Videos	Feb 13, 2020	Emotion RecognitionGeneral Classification	—Unverified
Emotion Recognition for Low-Resource Turkish: Fine-Tuning BERTurk on TREMO and Testing on Xenophobic Political Discourse	May 17, 2025	Decision MakingEmotion Recognition	—Unverified
Emotion Recognition for Vietnamese Social Media Text	Nov 21, 2019	Emotion RecognitionSentiment Analysis	—Unverified
Emotion Recognition From Gait Analyses: Current Research and Future Directions	Mar 13, 2020	Emotion Recognition	—Unverified
Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN	Jan 8, 2023	Emotion ClassificationEmotion Recognition	—Unverified
Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies	Aug 18, 2021	Domain AdaptationEmotional Intelligence	—Unverified
Emotion Recognition from Speech based on Relevant Feature and Majority Voting	Jul 11, 2018	BIG-bench Machine LearningEmotion Recognition	—Unverified
Emotion Recognition from the perspective of Activity Recognition	Mar 24, 2024	Action RecognitionActivity Recognition	—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