Spotting Agreement and Disagreement: A Survey of Nonverbal Audiovisual Cues and Tools

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

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

Fusing face and body gesture for machine recognition of emotions

Research shows that humans are more likely to consider computers to be human-like when those computers understand and display appropriate nonverbal communicative behavior. Most of the existing systems attempting to analyze the human nonverbal behavior focus only on the face; research that aims to integrate gesture as an expression mean has only recently emerged. This paper presents an approach to automatic visual recognition of expressive face and upper body action units (FAUs and BAUs) suitable for use in a vision-based affective multimodal framework. After describing the feature extraction techniques, classification results from three subjects are presented. Firstly, individual classifiers are trained separately with face and body features for classification into FAU and BAU categories. Secondly, the same procedure is applied for classification into labeled emotion categories. Finally, we fuse face and body information for classification into combined emotion categories. In our experiments, the emotion classification using the two modalities achieved a better recognition accuracy outperforming the classification using the individual face modality. © 2005 IEEE

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

Multimodal fusion : gesture and speech input in augmented reality environment

Augmented Reality (AR) has the capability to interact with the virtual objects and physical objects simultaneously since it combines the real world with virtual world seamlessly. However, most AR interface applies conventional Virtual Reality (VR) interaction techniques without modification. In this paper we explore the multimodal fusion for AR with speech and hand gesture input. Multimodal fusion enables users to interact with computers through various input modalities like speech, gesture, and eye gaze. At the first stage to propose the multimodal interaction, the input modalities are decided to be selected before be integrated in an interface. The paper presents several related works about to recap the multimodal approaches until it recently has been one of the research trends in AR. It presents the assorted existing works in multimodal for VR and AR. In AR, multimodal considers as the solution to improve the interaction between the virtual and physical entities. It is an ideal interaction technique for AR applications since AR supports interactions in real and virtual worlds in the real-time. This paper describes the recent studies in AR developments that appeal gesture and speech inputs. It looks into multimodal fusion and its developments, followed by the conclusion.This paper will give a guideline on multimodal fusion on how to integrate the gesture and speech inputs in AR environment