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 1901–1910 of 2041 papers

Title	Date	Tasks	Status
Benchmarking Multimodal Sentiment Analysis	Jul 29, 2017	BenchmarkingEmotion Recognition	—Unverified
Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition	Jul 26, 2017	Emotion RecognitionMicro Expression Recognition	—Unverified
Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices	Jul 22, 2017	Emotion ClassificationEmotion Recognition	—Unverified
Automatic Recognition of Facial Displays of Unfelt Emotions	Jul 13, 2017	Emotion RecognitionVideo Emotion Recognition	—Unverified
A breakthrough in Speech emotion recognition using Deep Retinal Convolution Neural Networks	Jul 12, 2017	Data AugmentationEmotion Recognition	—Unverified
Context-Dependent Sentiment Analysis in User-Generated Videos	Jul 1, 2017	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available
Emotion Recognition in Context	Jul 1, 2017	Emotion RecognitionEmotion Recognition in Context	—Unverified
Progressive Neural Networks for Transfer Learning in Emotion Recognition	Jun 10, 2017	Emotion RecognitionTransfer Learning	CodeCode Available
Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition	Jun 7, 2017	Emotion RecognitionEvent Detection	—Unverified
Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition	Jun 7, 2017	Emotion RecognitionMusic Emotion 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