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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1351–1360 of 2041 papers

Title	Date	Tasks	Status
Emotion pattern detection on facial videos using functional statistics	Mar 1, 2021	Emotion Recognition	—Unverified
Discriminative Multiple Canonical Correlation Analysis for Information Fusion	Feb 28, 2021	Emotion RecognitionHandwritten Digit Recognition	—Unverified
The Labeled Multiple Canonical Correlation Analysis for Information Fusion	Feb 28, 2021	Emotion RecognitionFace Recognition	—Unverified
Emotion recognition techniques with rule based and machine learning approaches	Feb 28, 2021	BIG-bench Machine LearningCultural Vocal Bursts Intensity Prediction	—Unverified
Towards Emotion Recognition in Hindi-English Code-Mixed Data: A Transformer Based Approach	Feb 19, 2021	Emotion RecognitionWord Embeddings	CodeCode Available
Contrastive Unsupervised Learning for Speech Emotion Recognition	Feb 12, 2021	Emotion RecognitionRepresentation Learning	—Unverified
Lie-Sensor: A Live Emotion Verifier or a Licensor for Chat Applications using Emotional Intelligence	Feb 11, 2021	Emotional IntelligenceEmotion Recognition	—Unverified
CASA-Based Speaker Identification Using Cascaded GMM-CNN Classifier in Noisy and Emotional Talking Conditions	Feb 11, 2021	Emotion RecognitionSpeaker Identification	—Unverified
Non-linear frequency warping using constant-Q transformation for speech emotion recognition	Feb 8, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified
ScalingNet: extracting features from raw EEG data for emotion recognition	Feb 7, 2021	EEGElectroencephalogram (EEG)	—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