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 351–360 of 2041 papers

Title	Date	Tasks	Status
A survey of smart classroom: Concept, technologies and facial emotions recognition application	Dec 3, 2022	Emotion RecognitionFacial Emotion Recognition	—Unverified
A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition	Jun 16, 2023	Emotion Recognition	—Unverified
Context-Dependent Models for Predicting and Characterizing Facial Expressiveness	Dec 10, 2019	Emotion Recognition	—Unverified
A Survey of Deep Learning for Group-level Emotion Recognition	Aug 13, 2024	Deep LearningEmotion Recognition	—Unverified
All in One: A Multi-Task Learning for Emoji, Sentiment and Emotion Analysis in Code-Mixed Text	Nov 16, 2021	AllEmotion Recognition	—Unverified
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics	Oct 1, 2021	ClassificationEmotion Classification	—Unverified
Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models	Apr 18, 2024	Emotion RecognitionFacial Expression Recognition	—Unverified
Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games	Mar 5, 2021	EEGElectroencephalogram (EEG)	—Unverified
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis	Jan 18, 2022	BIG-bench Machine LearningEmotion Recognition	—Unverified
A study on cross-corpus speech emotion recognition and data augmentation	Jan 10, 2022	Cross-corpusData Augmentation	—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