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 981–990 of 2041 papers

Title	Date	Tasks	Status
An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset	Apr 2, 2023	Emotion RecognitionFacial Emotion Recognition	—Unverified
Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition	Mar 29, 2023	Emotion ClassificationEmotion Recognition	—Unverified
Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition	Mar 28, 2023	Emotion RecognitionFacial Emotion Recognition	CodeCode Available
Depression detection in social media posts using affective and social norm features	Mar 24, 2023	Depression DetectionEmotion Recognition	—Unverified
CNN-n-GRU: end-to-end speech emotion recognition from raw waveform signal using CNNs and gated recurrent unit networks	Mar 23, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Efficient Neural Architecture Search for Emotion Recognition	Mar 23, 2023	Emotion RecognitionFacial Expression Recognition	—Unverified
EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation	Mar 20, 2023	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available
How People Respond to the COVID-19 Pandemic on Twitter: A Comparative Analysis of Emotional Expressions from US and India	Mar 19, 2023	Emotion Recognition	—Unverified
Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes	Mar 17, 2023	Emotion Recognition	CodeCode Available
EmotiEffNet Facial Features in Uni-task Emotion Recognition in Video at ABAW-5 competition	Mar 16, 2023	Action Unit DetectionArousal Estimation	—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