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 1351–1360 of 2041 papers

Title	Date	Tasks	Status
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis	Jun 2, 2016	Dimensionality ReductionEmotion Recognition	—Unverified
Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition	Oct 29, 2022	Emotion RecognitionMulti-Task Learning	—Unverified
UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause	Mar 30, 2024	Emotion Cause ExtractionEmotion-Cause Pair Extraction	—Unverified
Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion	Mar 31, 2025	Emotion RecognitionKnowledge Distillation	—Unverified
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion	Jul 25, 2023	Emotional IntelligenceEmotion Recognition	—Unverified
Unsupervised Counselor Dialogue Clustering for Positive Emotion Elicitation in Neural Dialogue System	Jul 1, 2018	ClusteringEmotion Recognition	—Unverified
Unsupervised Cross-Lingual Speech Emotion Recognition Using DomainAdversarial Neural Network	Dec 21, 2020	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Unsupervised Discovery of Facial Landmarks and Head Pose	Jan 1, 2025	Emotion RecognitionHead Pose Estimation	—Unverified
Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition	Nov 19, 2018	ClassificationEEG	—Unverified
Unsupervised low-rank representations for speech emotion recognition	Apr 14, 2021	Dimensionality ReductionEmotion Recognition	—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