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 1631–1640 of 2041 papers

Title	Date	Tasks	Status
Audio-Visual Compound Expression Recognition Method based on Late Modality Fusion and Rule-based Decision	Mar 19, 2024	Cross-corpusEmotion Recognition	—Unverified
Audio Visual Emotion Recognition with Temporal Alignment and Perception Attention	Mar 28, 2016	ClassificationEmotion Recognition	—Unverified
Augmenting Supervised Emotion Recognition with Rule-Based Decision Model	Jul 9, 2016	Emotion Recognition	—Unverified
A Unified Transformer-based Network for multimodal Emotion Recognition	Aug 27, 2023	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT	Apr 1, 2024	Emotion RecognitionSentiment Analysis	—Unverified
Automated Feature Extraction on AsMap for Emotion Classification using EEG	Jan 28, 2022	Automated Feature EngineeringClassification	—Unverified
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit	Jun 14, 2021	Active LearningBinary Classification	—Unverified
Automatic Detection of Sentimentality from Facial Expressions	Sep 11, 2022	Emotion Recognition	—Unverified
Automatic Emotion Recognition (AER) System based on Two-Level Ensemble of Lightweight Deep CNN Models	Apr 30, 2019	EEGElectroencephalogram (EEG)	—Unverified
Automatic Group Cohesiveness Detection With Multi-modal Features	Oct 2, 2019	Emotion Recognitionregression	—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