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 1361–1370 of 2041 papers

Title	Date	Tasks	Status
Zara The Supergirl: An Empathetic Personality Recognition System	Jun 1, 2016	Emotion RecognitionSentiment Analysis	—Unverified
Zero-Shot Emotion Recognition via Affective Structural Embedding	Oct 1, 2019	Emotion RecognitionZero-Shot Learning	—Unverified
HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images	Jul 19, 2022	Emotion RecognitionMulti-Task Learning	—Unverified
Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM	Jul 4, 2023	Emotion Recognitionfeature selection	—Unverified
HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding	Jan 25, 2025	Action UnderstandingEmotion Recognition	—Unverified
HUMIR at IEST-2018: Lexicon-Sensitive and Left-Right Context-Sensitive BiLSTM for Implicit Emotion Recognition	Oct 1, 2018	Emotion RecognitionFeature Engineering	—Unverified
Hybrid Attention based Multimodal Network for Spoken Language Classification	Aug 1, 2018	ClassificationDeep Learning	—Unverified
Hybrid Backpropagation Parallel Reservoir Networks	Oct 27, 2020	EEGElectroencephalogram (EEG)	—Unverified
Hybrid Curriculum Learning for Emotion Recognition in Conversation	Dec 22, 2021	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition	Sep 18, 2021	Data AugmentationDeep Attention	—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