Emotion Recognition
464 papers with code • 7 benchmarks • 45 datasets
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
Benchmarks
These leaderboards are used to track progress in Emotion Recognition
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Libraries
Use these libraries to find Emotion Recognition models and implementationsDatasets
Hateful Memes
FER2013
Aff-Wild
CARER
SEED
MSP-IMPROV
Aff-Wild2
ISEAR
Hateful Memes Challenge
EmotionLines
Subtasks
Latest papers with no code
TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition
One persistent challenge in Speech Emotion Recognition (SER) is the ubiquitous environmental noise, which frequently results in diminished SER performance in practical use.
Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models
Facial expression recognition is a pivotal component in machine learning, facilitating various applications.
Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities
The study of human emotions, traditionally a cornerstone in fields like psychology and neuroscience, has been profoundly impacted by the advent of artificial intelligence (AI).
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation
Using metric learning through a Siamese Network architecture, we achieve 57. 71 in macro F1 score for emotion classification in conversation on DailyDialog dataset, which outperforms the related work.
Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition
In this study, we propose a novel Joint Contrastive learning framework with Feature Alignment (JCFA) to address cross-corpus EEG-based emotion recognition.
Improving Personalisation in Valence and Arousal Prediction using Data Augmentation
This paper presents our work on an enhanced personalisation strategy, that leverages data augmentation to develop tailored models for continuous valence and arousal prediction.
Multimodal Emotion Recognition by Fusing Video Semantic in MOOC Learning Scenarios
The method proposed in this paper not only contributes to a deeper understanding of the impact of instructional videos on learners' emotional states but also provides a beneficial reference for future research on emotion recognition in MOOC learning scenarios.
The Power of Properties: Uncovering the Influential Factors in Emotion Classification
We introduce a workflow to evaluate explicit properties and their impact.
Dynamic Resolution Guidance for Facial Expression Recognition
Facial expression recognition (FER) is vital for human-computer interaction and emotion analysis, yet recognizing expressions in low-resolution images remains challenging.
Improving Facial Landmark Detection Accuracy and Efficiency with Knowledge Distillation
The domain of computer vision has experienced significant advancements in facial-landmark detection, becoming increasingly essential across various applications such as augmented reality, facial recognition, and emotion analysis.
