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 1961–1970 of 2041 papers

Title	Date	Tasks	Status
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark	May 9, 2016	BenchmarkingEmotion Recognition	CodeCode Available
AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge	May 5, 2016	Emotion Recognition	—Unverified
Construction of Japanese Audio-Visual Emotion Database and Its Application in Emotion Recognition	May 1, 2016	Emotion Recognition	—Unverified
Emotion Analysis on Twitter: The Hidden Challenge	May 1, 2016	BIG-bench Machine LearningEmotion Recognition	—Unverified
Could Speaker, Gender or Age Awareness be beneficial in Speech-based Emotion Recognition?	May 1, 2016	Emotion RecognitionSpoken Dialogue Systems	—Unverified
Facial expression recognition based on local region specific features and support vector machines	Apr 15, 2016	Emotion RecognitionFacial Expression Recognition	—Unverified
Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos	Apr 9, 2016	3D Human Pose EstimationEmotion Recognition	—Unverified
Exploiting Facial Landmarks for Emotion Recognition in the Wild	Mar 30, 2016	Emotion Recognition	—Unverified
Audio Visual Emotion Recognition with Temporal Alignment and Perception Attention	Mar 28, 2016	ClassificationEmotion Recognition	—Unverified
Modelling Temporal Information Using Discrete Fourier Transform for Recognizing Emotions in User-generated Videos	Mar 20, 2016	Emotion ClassificationEmotion 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