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 981–990 of 2041 papers

Title	Date	Tasks	Status
Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos	Apr 9, 2016	3D Human Pose EstimationEmotion Recognition	—Unverified
Fusion approaches for emotion recognition from speech using acoustic and text-based features	Mar 27, 2024	Emotion RecognitionWord Embeddings	—Unverified
Fusion of EEG and Musical Features in Continuous Music-emotion Recognition	Nov 30, 2016	EEGElectroencephalogram (EEG)	—Unverified
Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition	Sep 15, 2021	Emotion ClassificationEmotion Recognition	—Unverified
Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children	Aug 31, 2023	Audio Emotion RecognitionEmotional Intelligence	—Unverified
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition	Jan 26, 2025	Domain AdaptationEmotion Recognition	—Unverified
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations	Sep 8, 2023	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models	Jun 4, 2020	Data AugmentationEEG	—Unverified
Dual Prototyping with Domain and Class Prototypes for Affective Brain-Computer Interface in Unseen Target Conditions	Nov 27, 2024	Brain Computer InterfaceDisentanglement	—Unverified
Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement	Jun 19, 2021	DisentanglementEmotion Recognition	—Unverified

Show:10 25 50

← PrevPage 99 of 205Next →

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