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 1161–1170 of 2041 papers

Title	Date	Tasks	Status	Hype
Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly	Nov 9, 2021	Emotion RecognitionFacial Expression Recognition	—Unverified	0
Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition	Nov 9, 2021	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	1
Grassmannian learning mutual subspace method for image set recognition	Nov 8, 2021	Emotion RecognitionFace Identification	—Unverified	0
Global-Local Attention for Emotion Recognition	Nov 7, 2021	Emotion Recognition	CodeCode Available	1
Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks	Nov 5, 2021	Emotion RecognitionSentiment Analysis	—Unverified	0
EEG-Based Emotion Recognition Using Genetic Algorithm Optimized Multi-Layer Perceptron	Nov 4, 2021	EEGEEG Emotion Recognition	CodeCode Available	1
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding	Nov 4, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Facial Emotion Recognition using Deep Residual Networks in Real-World Environments	Nov 4, 2021	Emotion RecognitionFacial Emotion Recognition	—Unverified	0
A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition	Nov 3, 2021	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	1
Multi-Cue Adaptive Emotion Recognition Network	Nov 3, 2021	Emotion 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