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 311–320 of 2041 papers

Title	Date	Tasks	Status	Hype
Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition	Nov 11, 2018	Emotion Recognition	CodeCode Available	1
Survey on Emotional Body Gesture Recognition	Jan 23, 2018	Cultural Vocal Bursts Intensity PredictionEmotion Recognition	CodeCode Available	1
How Deep Neural Networks Can Improve Emotion Recognition on Video Data	Feb 24, 2016	Emotion Recognition	CodeCode Available	1
Training Deep Neural Networks on Noisy Labels with Bootstrapping	Dec 20, 2014	Emotion RecognitionObject Recognition	CodeCode Available	1
Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation	Jul 21, 2025	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	0
Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context	Jul 17, 2025	Emotion Recognition	—Unverified	0
A Robust Incomplete Multimodal Low-Rank Adaptation Approach for Emotion Recognition	Jul 15, 2025	Emotion RecognitionMultimodal Emotion Recognition	—Unverified	0
Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation	Jul 11, 2025	4kEmotion Recognition	CodeCode Available	0
CAST-Phys: Contactless Affective States Through Physiological signals Database	Jul 8, 2025	Emotion RecognitionPhotoplethysmography (PPG)	—Unverified	0
How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?	Jun 25, 2025	Emotion RecognitionIn-Context Learning	—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