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 621–630 of 2041 papers

Title	Date	Tasks	Status
Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition	Apr 28, 2020	Emotion Recognition	—Unverified
Addressing Racial Bias in Facial Emotion Recognition	Aug 9, 2023	Emotion RecognitionFacial Emotion Recognition	—Unverified
Decoding Human Emotions: Analyzing Multi-Channel EEG Data using LSTM Networks	Aug 19, 2024	EEGElectroencephalogram (EEG)	—Unverified
Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition	Aug 4, 2021	ClassificationEmotion Classification	—Unverified
Decoding Emotional Experience through Physiological Signal Processing	Jun 1, 2016	Dimensionality ReductionEmotion Recognition	—Unverified
Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech	Jun 2, 2017	Emotion RecognitionMULTI-VIEW LEARNING	—Unverified
Deciphering Emotions in Children Storybooks: A Comparative Analysis of Multimodal LLMs in Educational Applications	Jun 22, 2025	Emotion Recognition	—Unverified
DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks	Apr 29, 2025	Contrastive LearningEEG	—Unverified
Attention Driven Fusion for Multi-Modal Emotion Recognition	Sep 23, 2020	Emotion ClassificationEmotion Recognition	—Unverified
Data Fine-tuning	Dec 10, 2018	AttributeEmotion 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