Visual Nonverbal Behavior Analysis: The Path Forward

摘要

Social signal processing (SSP) is a promising automated technology that aims to provide computers with the ability to sense and understand human social behaviors. Representative SSP applications include novel human-computer interaction mechanisms that enhance machine sensitivity of users emotional and mental states, more engaging games, ambient intelligence systems responsive to social context, and new quantitative psychological evaluation tools for coaching or diagnosis. Based on adopted cues, existing SSP methods can be categorized as verbal or nonverbal. Over the last decade, significant progress has been accomplished in visual nonverbal behavior analysis (VNBA). However, several emerging issues such as fusion of multimodal cues, context estimation, and user privacy protection still need to be addressed adequately. The authors present an overview of VNBA and describe various research challenges and proposed solutions.