While detecting and interpreting temporal patterns of non–verbal behavioral cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human–centered interaction they are bound to experience. This paper surveys the nonverbal cues that could be present during agreement and disagreement behavioural displays and lists a number of tools that could be useful in detecting them, as well as a few publicly available databases that could be used to train these tools for analysis of spontaneous, audiovisual instances of agreement and disagreement. 1.

WE HAVE entered an era of enhanced digital connectivity. Computers and the Internet have become so embedded in the daily fabric of people’s lives that people simply cannot live without them. We use this technology to work, to communicate, to shop, to seek out new information, and to entertain ourselves. In other words, computers are becoming full social actors that need to interact with people as seamlessly as possible. The key to development of computers as such social actors is to design human–computer interaction (HCI) that is human centered, built for humans based on human behavior models [1], [2]. In other words, HCI designs should focus on the human portion of the HCI context rather than on the computer portion, as was the case in classic HCI designs such as direct manipulation and delegation. They should transcend the traditional keyboard and mouse to include natural

We introduce a new approach to analyzing the attentive state of a human subject, given cameras focused on the subject and their environment. In particular, the task of analyzing the focus of attention of a human driver is of primary concern. Up to 80 % of automobile crashes are related to driver inattention; thus it is important for an Intelligent Driver Assistance System (IDAS) to be aware of the driver state. We present a new Bayesian paradigm for estimating human attention specifically addressing the problems arising in dynamic situations. The model incorporates vision-based gaze estimation, “top-down”- and “bottomup”-based visual saliency maps, and cognitive considerations such as inhibition of return and center bias that affect the relationship between gaze and attention. Results demonstrate the validity on real driving data, showing quantitative improvements over systems using only gaze or only saliency, and elucidate the value of such a model for any human-machine interface. 1.

When a robot is situated in an environment containing multiple possible interaction partners, it has to make decisions about when to engage specific users and how to detect and react appropriately to actions of the users that might signal the intention to interact. In this demonstration we present the integration of an engagement model in an existing dialog system based on interaction patterns. As a sample scenario, this enables the humanoid robot Nao to play a quiz game with multiple participants. 1

WE HAVE entered an era of enhanced digital connectivity. Computers and the Internet have become so embedded in the daily fabric of people’s lives that people simply cannot live without them. We use this technology to work, to communicate, to shop, to seek out new information, and to entertain ourselves. In other words, computers are becoming full social actors that need to interact with people as seamlessly as possible. The key to development of computers as such social actors is to design human–computer interaction (HCI) that is human centered, built for humans based on human behavior models [1], [2]. In other words, HCI designs should focus on the human portion of the HCI context rather than on the computer portion, as was the case in classic HCI designs such as direct manipulation and delegation. They should transcend the traditional keyboard and mouse to include natural