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 611–620 of 2041 papers

Title	Date	Tasks	Status	Score
EmotionX-KU: BERT-Max based Contextual Emotion Classifier	Jun 27, 2019	Emotion RecognitionLanguage Modeling	CodeCode Available	5
Improving Speech Emotion Recognition Through Cross Modal Attention Alignment and Balanced Stacking Model	May 26, 2025	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
FEALLM: Advancing Facial Emotion Analysis in Multimodal Large Language Models with Emotional Synergy and Reasoning	May 19, 2025	Emotion Recognition	CodeCode Available	5
Multitask Learning and Multistage Fusion for Dimensional Audiovisual Emotion Recognition	Feb 26, 2020	AttributeEmotion Recognition	CodeCode Available	5
Deep Evolution for Facial Emotion Recognition	Sep 29, 2020	Emotion RecognitionEvolutionary Algorithms	—Unverified	0
AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition	Jun 9, 2022	Emotion RecognitionRepresentation Learning	—Unverified	0
Attributes-aware Visual Emotion Representation Learning	Apr 9, 2025	AttributeEmotion Recognition	—Unverified	0
A Multimodal Emotion Recognition System: Integrating Facial Expressions, Body Movement, Speech, and Spoken Language	Dec 23, 2024	DiagnosticEmotion Recognition	—Unverified	0
Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly	Nov 9, 2021	Emotion RecognitionFacial Expression Recognition	—Unverified	0
Deep CNN with late fusion for realtime multimodal emotion recognition	Apr 15, 2024	Emotion RecognitionMultimodal Emotion Recognition	—Unverified	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