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 921–930 of 2041 papers

Title	Date	Tasks	Status
Facial Emotion Recognition Using Deep Learning	Oct 19, 2019	Deep LearningEmotion Recognition	—Unverified
Facial Emotion Recognition using Deep Residual Networks in Real-World Environments	Nov 4, 2021	Emotion RecognitionFacial Emotion Recognition	—Unverified
Emotion Recognition from the perspective of Activity Recognition	Mar 24, 2024	Action RecognitionActivity Recognition	—Unverified
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface	Sep 7, 2022	Brain Computer InterfaceEmotion Recognition	—Unverified
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition	Jun 12, 2022	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Facial Emotions Recognition using Convolutional Neural Net	Jan 6, 2020	Emotion RecognitionFace Detection	—Unverified
Emotion Recognition from Speech based on Relevant Feature and Majority Voting	Jul 11, 2018	BIG-bench Machine LearningEmotion Recognition	—Unverified
Facial Expression Recognition and Image Description Generation in Vietnamese	Aug 12, 2022	DescriptiveEmotion Recognition	—Unverified
COIN: Conversational Interactive Networks for Emotion Recognition in Conversation	Jun 1, 2021	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
A Graph Isomorphism Network with Weighted Multiple Aggregators for Speech Emotion Recognition	Jul 3, 2022	Emotion RecognitionGraph Neural Network	—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