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 1951–1960 of 2041 papers

Title	Date	Tasks	Status
Multimodal Affect Recognition using Kinect	Jul 9, 2016	Emotion Recognition	—Unverified
Fractal Dimension Pattern Based Multiresolution Analysis for Rough Estimator of Person-Dependent Audio Emotion Recognition	Jul 1, 2016	Audio Emotion RecognitionEmotion Recognition	—Unverified
Bangla Parts-of-Speech Tagging using Bangla Stemmer and Rule based Analyzer	Jun 9, 2016	Emotion RecognitionInformation Retrieval	CodeCode Available
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis	Jun 2, 2016	Dimensionality ReductionEmotion Recognition	—Unverified
I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter	Jun 1, 2016	Emotion RecognitionGeneral Classification	—Unverified
Zara The Supergirl: An Empathetic Personality Recognition System	Jun 1, 2016	Emotion RecognitionSentiment Analysis	—Unverified
Decoding Emotional Experience through Physiological Signal Processing	Jun 1, 2016	Dimensionality ReductionEmotion Recognition	—Unverified
openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit	May 22, 2016	Document ClassificationEmotion Recognition	CodeCode Available
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

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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