Call for papers
There is an ever-growing research interests of the computer vision and machine learning community in modeling human facial and gestural behavior for clinical applications. However, the current state of the art in computer vision and machine learning for face and gesture analysis has not yet achieved the goal of reliable use of behavioral indicators in clinical context. One main challenge to achieve this goal is the lack of available archives of behavioral observations of individuals that have clinically relevant conditions (e.g., pain, depression, autism spectrum). Well-labeled recordings of clinically relevant conditions are necessary to train classifiers. Interdisciplinary efforts to address this necessity are needed.
The workshop aims to discuss the strengths and major challenges in using computer vision and machine learning of automatic face and gesture analysis for clinical research and healthcare applications. We invite scientists working in related areas of computer vision and machine learning for face and gesture analysis, affective computing, human behavior sensing, and cognitive behavior to share their expertise and achievements in the emerging field of computer vision and machine learning based face and gesture analysis for health informatics.
Topics of interest include, but are not limited to:
- Facial expression analysis and recognition for healthcare
- Human gesture (head and body movement) analysis for healthcare
- Remote physiological sensing for healthcare
- Human-Computer Interaction systems for healthcare
- Deep learning based multi-modal signal analysis for healthcare applications
- Multi-modal (visual, verbal, and/or physiological) fusion for healthcare applications
- Clinical protocols for face and gesture analysis and modeling in clinical context
- Applications include but are not limited to: pain intensity measurement, assessment of depression symptoms and severity, assessment of anxiety symptoms, bipolar disorder severity assessment, and autism screening.