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 1611–1620 of 2041 papers

Title	Date	Tasks	Status
Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition	May 28, 2024	AnatomyEEG	—Unverified
Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation	Oct 4, 2023	Contrastive LearningEmotion Recognition	—Unverified
Multimodal Relational Tensor Network for Sentiment and Emotion Classification	Jun 7, 2018	ClassificationEmotion Classification	—Unverified
Multimodal Representation Learning Techniques for Comprehensive Facial State Analysis	Apr 14, 2025	Emotion RecognitionRepresentation Learning	—Unverified
Multi-modal Residual Perceptron Network for Audio-Video Emotion Recognition	Jul 21, 2021	Emotion RecognitionVideo Emotion Recognition	—Unverified
Multimodal Sentiment Analysis based on Video and Audio Inputs	Dec 12, 2024	Decision MakingEmotion Recognition	—Unverified
Multi-Modal Sequence Fusion via Recursive Attention for Emotion Recognition	Oct 1, 2018	Emotion Recognition	—Unverified
Multimodal Speech Emotion Recognition using Cross Attention with Aligned Audio and Text	Jul 26, 2022	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Multimodal Stress Detection Using Facial Landmarks and Biometric Signals	Nov 6, 2023	Emotion RecognitionFacial Emotion Recognition	—Unverified
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning	Aug 6, 2019	ClassificationEmotion 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