Complex Facial Expression Recognition

Complex facial expression recognition aims to move beyond basic emotion categorization (e.g., happiness, sadness) to accurately identify nuanced and often simultaneous emotional states. Current research heavily utilizes deep learning, particularly convolutional neural networks, often incorporating multimodal data (e.g., EEG, ECG) to improve accuracy and address the challenges of limited and complex datasets. This field is crucial for advancing affective computing applications, such as improving online learning engagement and enhancing human-computer interaction, as well as for clinical applications in mental health assessment.