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 1721–1730 of 2041 papers

Title	Date	Tasks	Status
A dataset of continuous affect annotations and physiological signals for emotion analysis	Dec 6, 2018	Emotion Recognition	—Unverified
Modality-based Factorization for Multimodal Fusion	Nov 30, 2018	Emotion RecognitionPersonality Trait Recognition	—Unverified
Two-level Attention with Two-stage Multi-task Learning for Facial Emotion Recognition	Nov 29, 2018	Emotion RecognitionFacial Emotion Recognition	—Unverified
Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos	Nov 28, 2018	Emotion Recognition	—Unverified
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness	Nov 28, 2018	Adversarial AttackBIG-bench Machine Learning	—Unverified
A Compact Embedding for Facial Expression Similarity	Nov 27, 2018	Action Unit DetectionEmotion Recognition	CodeCode Available
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors	Nov 23, 2018	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available
Towards Emotion Recognition: A Persistent Entropy Application	Nov 21, 2018	ClassificationEmotion Classification	—Unverified
PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship	Nov 21, 2018	Emotion RecognitionMulti-Task Learning	CodeCode Available
Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks	Nov 19, 2018	Emotion RecognitionMULTI-VIEW LEARNING	—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