# Emotion and Mood Recognition

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/emotion-and-mood-recognition/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the recognition and analysis of emotions from various modalities such as facial expressions, physiological signals, and speech. It explores the use of deep learning, affective computing, and multimodal data for emotion recognition in applications including human-computer interaction. |
| Domain | Social Sciences |
| Field | Psychology |
| OpenAlex ID | t10667 |
| Works | 130 |

## Topic papers all

Showing 15 of 130.

- [A review of affective computing: From unimodal analysis to multimodal fusion](https://scholariq.org/papers/a-review-of-affective-computing-from-unimodal-analysis-to-multimodal-fusion/)
- [Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions](https://scholariq.org/papers/multimodal-sentiment-analysis-a-systematic-review-of-history-datasets-multimodal/)
- [Emotion Recognition From EEG Using Higher Order Crossings](https://scholariq.org/papers/emotion-recognition-from-eeg-using-higher-order-crossings/)
- [Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis](https://scholariq.org/papers/convolutional-mkl-based-multimodal-emotion-recognition-and-sentiment-analysis/)
- [Fusing audio, visual and textual clues for sentiment analysis from multimodal content](https://scholariq.org/papers/fusing-audio-visual-and-textual-clues-for-sentiment-analysis-from-multimodal/)
- [The Berlin Affective Word List Reloaded (BAWL-R)](https://scholariq.org/papers/the-berlin-affective-word-list-reloaded-bawl-r/)
- [StressSense](https://scholariq.org/papers/stresssense/)
- [Automated assessment of psychiatric disorders using speech: A systematic review](https://scholariq.org/papers/automated-assessment-of-psychiatric-disorders-using-speech-a-systematic-review/)
- [Emotional responses to music: experience, expression, and physiology](https://scholariq.org/papers/emotional-responses-to-music-experience-expression-and-physiology/)
- [A Comprehensive Review of Speech Emotion Recognition Systems](https://scholariq.org/papers/a-comprehensive-review-of-speech-emotion-recognition-systems/)
- [Stress and anxiety detection using facial cues from videos](https://scholariq.org/papers/stress-and-anxiety-detection-using-facial-cues-from-videos/)
- [Island Loss for Learning Discriminative Features in Facial Expression Recognition](https://scholariq.org/papers/island-loss-for-learning-discriminative-features-in-facial-expression/)
- [Cardiac activation during arousal in humans: further evidence for hierarchy in the arousal response](https://scholariq.org/papers/cardiac-activation-during-arousal-in-humans-further-evidence-for-hierarchy-in/)
- [Human emotion recognition using deep belief network architecture](https://scholariq.org/papers/human-emotion-recognition-using-deep-belief-network-architecture/)
- [Functional imbalance of visual pathways indicates alternative face processing strategies in autism](https://scholariq.org/papers/functional-imbalance-of-visual-pathways-indicates-alternative-face-processing/)

## Topic primary papers

Showing 15 of 63.

- [A review of affective computing: From unimodal analysis to multimodal fusion](https://scholariq.org/papers/a-review-of-affective-computing-from-unimodal-analysis-to-multimodal-fusion/)
- [StressSense](https://scholariq.org/papers/stresssense/)
- [A Comprehensive Review of Speech Emotion Recognition Systems](https://scholariq.org/papers/a-comprehensive-review-of-speech-emotion-recognition-systems/)
- [Stress and anxiety detection using facial cues from videos](https://scholariq.org/papers/stress-and-anxiety-detection-using-facial-cues-from-videos/)
- [Island Loss for Learning Discriminative Features in Facial Expression Recognition](https://scholariq.org/papers/island-loss-for-learning-discriminative-features-in-facial-expression/)
- [Human emotion recognition using deep belief network architecture](https://scholariq.org/papers/human-emotion-recognition-using-deep-belief-network-architecture/)
- [Automatic Assessment of Depression Based on Visual Cues: A Systematic Review](https://scholariq.org/papers/automatic-assessment-of-depression-based-on-visual-cues-a-systematic-review/)
- [Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks](https://scholariq.org/papers/facial-expression-recognition-using-local-gravitational-force-descriptor-based/)
- [EMOVO Corpus: an Italian Emotional Speech Database](https://scholariq.org/papers/emovo-corpus-an-italian-emotional-speech-database/)
- [K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations](https://scholariq.org/papers/k-emocon-a-multimodal-sensor-dataset-for-continuous-emotion-recognition-in/)
- [FER-net: facial expression recognition using deep neural net](https://scholariq.org/papers/fer-net-facial-expression-recognition-using-deep-neural-net/)
- [Understanding Deep Learning Techniques for Recognition of Human Emotions Using Facial Expressions: A Comprehensive Survey](https://scholariq.org/papers/understanding-deep-learning-techniques-for-recognition-of-human-emotions-using/)
- [Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier](https://scholariq.org/papers/human-computer-interaction-for-recognizing-speech-emotions-using-multilayer/)
- [FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos](https://scholariq.org/papers/ferv39k-a-large-scale-multi-scene-dataset-for-facial-expression-recognition-in/)
- [Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures](https://scholariq.org/papers/dementia-detection-from-speech-using-machine-learning-and-deep-learning/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.