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 351–360 of 2041 papers

Title	Date	Tasks	Status	Hype
MDPE: A Multimodal Deception Dataset with Personality and Emotional Characteristics	Jul 17, 2024	Deception DetectionEmotion Recognition	CodeCode Available	1
Enhancing Facial Expression Recognition through Dual-Direction Attention Mixed Feature Networks: Application to 7th ABAW Challenge	Jul 17, 2024	Emotion RecognitionFacial Expression Recognition	—Unverified	0
MASIVE: Open-Ended Affective State Identification in English and Spanish	Jul 16, 2024	Emotion RecognitionMachine Translation	CodeCode Available	0
Large Vision-Language Models as Emotion Recognizers in Context Awareness	Jul 16, 2024	Emotion RecognitionIn-Context Learning	—Unverified	0
PSO Fuzzy XGBoost Classifier Boosted with Neural Gas Features on EEG Signals in Emotion Recognition	Jul 13, 2024	Decision MakingEEG	—Unverified	0
Emotion Detection through Body Gesture and Face	Jul 13, 2024	Emotion ClassificationEmotion Recognition	—Unverified	0
SensEmo: Enabling Affective Learning through Real-time Emotion Recognition with Smartwatches	Jul 13, 2024	Emotion Recognition	—Unverified	0
Enhancing Emotion Recognition in Incomplete Data: A Novel Cross-Modal Alignment, Reconstruction, and Refinement Framework	Jul 12, 2024	Contrastive Learningcross-modal alignment	—Unverified	0
Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models	Jul 9, 2024	coreference-resolutionCoreference Resolution	CodeCode Available	0
Analyzing Speech Unit Selection for Textless Speech-to-Speech Translation	Jul 8, 2024	Automatic Speech RecognitionEmotion 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