Real-time head nod and shake detection for continuous human affect recognition

Human affect recognition is the field of study associated with using automatic techniques to identify human emotion or human affective state. A person’s affective states is often communicated non-verbally through body language. A large part of human body language communication is the use of head gestures. Almost all cultures use subtle head movements to convey meaning. Two of the most common and distinct head gestures are the head nod and the head shake gestures. In this paper we present a robust system to automatically detect head nod and shakes. We employ the Microsoft Kinect and utilise discrete Hidden Markov Models (HMMs) as the backbone to a to a machine learning based classifier within the system. The system achieves 86% accuracy on test datasets and results are provided

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

String-based audiovisual fusion of behavioural events for the assessment of dimensional affect

The automatic assessment of affect is mostly based on feature-level approaches, such as distances between facial points or prosodic and spectral information when it comes to audiovisual analysis. However, it is known and intuitive that behavioural events such as smiles, head shakes or laughter and sighs also bear highly relevant information regarding a subject's affective display. Accordingly, we propose a novel string-based prediction approach to fuse such events and to predict human affect in a continuous dimensional space. Extensive analysis and evaluation has been conducted using the newly released SEMAINE database of human-to-agent communication. For a thorough understanding of the obtained results, we provide additional benchmarks by more conventional feature-level modelling, and compare these and the string-based approach to fusion of signal-based features and string-based events. Our experimental results show that the proposed string-based approach is the best performing approach for automatic prediction of Valence and Expectation dimensions, and improves prediction performance for the other dimensions when combined with at least acoustic signal-based features

Autonomous agents and avatars in REVERIE’s virtual environment

In this paper, we describe the enactment of autonomous agents and avatars in the web-based social collaborative virtual environment of REVERIE that supports natural, human-like behavior, physical interaction and engagement. Represented by avatars, users feel immersed in this virtual world in which they can meet and share experiences as in real life. Like the avatars, autonomous agents that may act in this world are capable of demonstrating human-like non-verbal behavior and facilitate social interaction. We describe how reasoning components of the REVERIE system connect and cooperatively control autonomous agents and avatars representing a user

A real-time human-robot interaction system based on gestures for assistive scenarios

Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times.Postprint (author's final draft