Novel applications of affective computing have emerged in recent years in domains ranging from health care to the 5th generation mobile network. Many of these have found improved emotion classification performance when fusing multiple sources of data (e.g., audio, video, brain, face, thermal, physiological, environmental, positional, text, etc.). Multimodal affect recognition has the potential to revolutionize the way various industries and sectors utilize information gained from recognition of a person’s emotional state, particularly considering the flexibility in the choice of modalities and measurement tools (e.g., surveillance versus mobile device cameras). Multimodal classification methods have been proven highly effective at minimizing misclassification error in practice and in dynamic conditions. Further, multimodal classification models tend to be more stable over time compared to relying on a single modality, increasing their reliability in sensitive applications such as mental health monitoring and automobile driver state recognition. To continue the trend of lab to practice within the field and encourage new applications of affective computing, this workshop provides a forum for the exchange of ideas on future direction, including novel fusion methods and databases, innovations through interdisciplinary research, and emerging emotion sensing devices.
Website:
http://www.csee.usf.edu/~tjneal/AMAR2020/index.html.
Organizers:
- Shaun Canavan
- Tempestt Neal
- Marvin Andujar
- Lijun Yin
