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 521–530 of 2041 papers

Title	Date	Tasks	Status
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition	Apr 13, 2021	Emotion RecognitionFeature Engineering	—Unverified
A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks	Jan 13, 2025	Deep LearningEmotion Recognition	—Unverified
Acquiring a Dictionary of Emotion-Provoking Events	Apr 1, 2014	Emotion Recognition	—Unverified
FE-Adapter: Adapting Image-based Emotion Classifiers to Videos	Aug 5, 2024	Dynamic Facial Expression RecognitionEmotion Recognition	—Unverified
Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition	Apr 15, 2021	Emotion RecognitionFacial Expression Recognition	—Unverified
Domain adversarial learning for emotion recognition	Oct 24, 2019	Emotion Recognition	—Unverified
EEG emotion recognition using dynamical graph convolutional neural networks	Mar 21, 2018	EEGEEG Emotion Recognition	—Unverified
Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data	May 25, 2023	Emotion RecognitionSentiment Analysis	—Unverified
A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition	Sep 21, 2020	EEGEEG Emotion Recognition	—Unverified
ComFace: Facial Representation Learning with Synthetic Data for Comparing Faces	May 25, 2024	Emotion RecognitionRepresentation Learning	—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