A review of motion analysis methods for human Nonverbal Communication Computing

a r t i c l e i n f o Human Nonverbal Communication Computing aims to investigate how people exploit nonverbal aspects of their communication to coordinate their activities and social relationships. Nonverbal behavior plays important roles in message production and processing, relational communication, social interaction and networks, deception and impression management, and emotional expression. This is a fundamental yet challenging research topic. To effectively analyze Nonverbal Communication Computing, motion analysis methods have been widely investigated and employed. In this paper, we introduce the concept and applications of Nonver-bal Communication Computing and also review some of the motion analysis methods employed in this area. They include face tracking, expression recognition, body reconstruction, and group activity analysis. In addition , we also discuss some open problems and the future directions of this area. Understanding how people exploit nonverbal aspects of their communication to coordinate their activities and social relationships is a fundamental scientific challenge. Deeper insights into nonverbal communication can have a profound impact on how we link theories of perception, learning, cognition and action to models of interactions and groups at the social level. Models of nonverbal behaviors in interaction are essential for collaboration tools, human–computer and virtual interaction and other assistive technologies designed to support people in real-world activities. This knowledge is also useful to develop models of the deficits of specific populations, such as autistic children , and interventions that bring them into fuller participation in communities. In general, nonverbal communication research offers high-level principles that might explain how people organize, display, adapt and understand such behaviors for communicative purposes and social goals. However, the specifics are generally not fully understood , nor is the way to translate these principles into algorithms and computer-aided communication technologies such as intelligent agents. To model such complex dynamic processes effectively, novel computer vision and learning algorithms are needed that take into account both the heterogeneity and the dynamicity intrinsic to behavior data. As one of the most active research areas in computer vision, human motion analysis has become a widely-used tool in this area. It uses image sequences to detect and track people, and also to interpret human activities. Emerging automated methods for analyzing motion [1] have been studied and developed to enable tracking diverse human movements precisely and robustly as well as correlating multiple people's movements in interaction. Some of the applications of using motion analysis methods for Nonverbal …