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 1861–1870 of 2041 papers

Title	Date	Tasks	Status
Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training	Oct 21, 2020	Domain AdaptationEmotion Recognition	—Unverified
Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities	Apr 18, 2024	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Dynamic Model of Facial Expression Recognition based on Eigen-face Approach	Nov 23, 2013	Emotion RecognitionFacial Expression Recognition	—Unverified
Dynamic Resolution Guidance for Facial Expression Recognition	Apr 9, 2024	Emotion RecognitionFacial Expression Recognition	—Unverified
Dynamic Time-Alignment of Dimensional Annotations of Emotion using Recurrent Neural Networks	Sep 21, 2022	Emotion Recognition	—Unverified
標記對於類神經語音情緒辨識系統辨識效果之影響(Effects of Label in Neural Speech Emotion Recognition System)[In Chinese]	Oct 1, 2016	Emotion RecognitionSpeech Emotion Recognition	—Unverified
EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model	May 1, 2024	De-identificationEmotion Recognition	—Unverified
Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better	Sep 12, 2024	Emotion RecognitionLanguage Modeling	—Unverified
ECG-EmotionNet: Nested Mixture of Expert (NMoE) Adaptation of ECG-Foundation Model for Driver Emotion Recognition	Mar 3, 2025	Autonomous DrivingComputational Efficiency	—Unverified
Edge Based Grid Super-Imposition for Crowd Emotion Recognition	Aug 7, 2016	Edge DetectionEmotion 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