Emotion and Mood Recognition

Emotion and mood recognition research investigates how human affective states can be detected and interpreted from signals such as facial expressions, voice patterns, and physiological measures like heart rate or skin conductance. Because no single signal reliably captures the full complexity of an emotional experience, a central effort involves combining these modalities and training deep learning models to integrate them in ways that are robust across individuals, contexts, and cultures. The work has practical stakes in human-computer interaction, mental health monitoring, and adaptive systems that respond to a user's emotional state in real time. Open challenges include moving beyond laboratory conditions to naturalistic settings, handling the inherent subjectivity in how emotions are labeled, and ensuring that recognition systems perform equitably across diverse populations.

Works

51,365

Total citations

514,798

Keywords

Emotion RecognitionMultimodal DataFacial ExpressionPhysiological SignalsDeep LearningAffective Computing