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 1971–1980 of 2041 papers

Title	Date	Tasks	Status
Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application	Jan 19, 2016	Emotion Recognition	—Unverified
TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth	Jan 16, 2016	Emotion RecognitionSentiment Analysis	—Unverified
Identifying Stable Patterns over Time for Emotion Recognition from EEG	Jan 10, 2016	BIG-bench Machine LearningEEG	—Unverified
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression	Dec 27, 2015	Emotion RecognitionPoint Tracking	—Unverified
The Indian Spontaneous Expression Database for Emotion Recognition	Dec 3, 2015	Emotion RecognitionFacial Expression Recognition	—Unverified
Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization	Nov 16, 2015	Emotion RecognitionTransfer Learning	—Unverified
結合非線性動態特徵之語音情緒辨識(Speech Emotion Recognition via Nonlinear Dynamical Features)[In Chinese]	Oct 1, 2015	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Hybrid Method of Semi-supervised Learning and Feature Weighted Learning for Domain Adaptation of Document Classification	Oct 1, 2015	Document ClassificationDomain Adaptation	—Unverified
Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features	Oct 1, 2015	Emotion ClassificationEmotion Recognition	—Unverified
DeXpression: Deep Convolutional Neural Network for Expression Recognition	Sep 17, 2015	Emotion RecognitionFacial Expression Recognition	CodeCode Available

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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