Gaze Behavior, Believability, Likability and the iCat

The iCat is a user-interface robot with the ability to express a range of emotions through its facial features. This paper summarizes our research whether we can increase the believability and likability of the iCat for its human partners through the application of gaze behaviour. Gaze behaviour serves several functions during social interaction such as mediating conversation flow, communicating emotional information and avoiding distraction by restricting visual input. There are several types of eye and head movements that are necessary for realizing these functions. We designed and evaluated a gaze behaviour system for the iCat robot that implements realistic models of the major types of eye and head movements found in living beings: vergence, vestibulo ocular reflexive, smooth pursuit movements and gaze shifts. We discuss how these models are integrated into the software environment of the iCat and can be used to create complex interaction scenarios. We report about some user tests and draw conclusions for future evaluation scenarios

Coordinated Eye and Head Movements for Gaze Interaction in 3D Environments

Gaze is attractive for interaction, as we naturally look at objects we are interested in. As a result, gaze has received significant attention within human-computer interaction as an input modality. However, gaze has been limited to only eye movements in situations where head movements are not expected to be used or as head movements in an approximation of gaze when an eye tracker is unavailable. From these observations arise an opportunity and a challenge: we propose to consider gaze as multi-modal in line with psychology and neuroscience research to more accurately represent user movements. The natural coordination of eye and head movements could then enable the development of novel interaction techniques to further the possibilities of gaze as an input modality. However, knowledge of the eye and head coordination in 3D environments and its usage for interaction design is limited. This thesis explores eye and head coordination and their potential for interaction in 3D environments by developing interaction techniques that aim to tackle established gaze-interaction issues. We study fundamental eye, head, and body movements in virtual reality during gaze shifts. From the study results, we design interaction techniques and applications that avoid the Midas touch issue, allow expressive gaze- based interaction, and handle eye tracking accuracy issues. We ground the evaluation of our interaction techniques through empirical studies. From the techniques and study results, we define three design principles for coordinated eye and head interaction from these works that distinguish between eye- only and head-supported gaze shifts, eye-head alignment as input, and distinguishing head movements for gestures and head movements that naturally occur to support gaze. We showcase new directions for gaze-based interaction and present a new way to think about gaze by taking a more comprehensive approach to gaze interaction and showing that there is more to gaze than just the eyes

Real-time gaze estimation using a Kinect and a HD webcam

In human-computer interaction, gaze orientation is an important and promising source of information to demonstrate the attention and focus of users. Gaze detection can also be an extremely useful metric for analysing human mood and affect. Furthermore, gaze can be used as an input method for human-computer interaction. However, currently real-time and accurate gaze estimation is still an open problem. In this paper, we propose a simple and novel estimation model of the real-time gaze direction of a user on a computer screen. This method utilises cheap capturing devices, a HD webcam and a Microsoft Kinect. We consider that the gaze motion from a user facing forwards is composed of the local gaze motion shifted by eye motion and the global gaze motion driven by face motion. We validate our proposed model of gaze estimation and provide experimental evaluation of the reliability and the precision of the method

An investigation into gaze-based interaction techniques for people with motor impairments

The use of eye movements to interact with computers offers opportunities for people with impaired motor ability to overcome the difficulties they often face using hand-held input devices. Computer games have become a major form of entertainment, and also provide opportunities for social interaction in multi-player environments. Games are also being used increasingly in education to motivate and engage young people. It is important that young people with motor impairments are able to benefit from, and enjoy, them. This thesis describes a program of research conducted over a 20-year period starting in the early 1990's that has investigated interaction techniques based on gaze position intended for use by people with motor impairments. The work investigates how to make standard software applications accessible by gaze, so that no particular modification to the application is needed. The work divides into 3 phases. In the first phase, ways of using gaze to interact with the graphical user interfaces of office applications were investigated, designed around the limitations of gaze interaction. Of these, overcoming the inherent inaccuracies of pointing by gaze at on-screen targets was particularly important. In the second phase, the focus shifted from office applications towards immersive games and on-line virtual worlds. Different means of using gaze position and patterns of eye movements, or gaze gestures, to issue commands were studied. Most of the testing and evaluation studies in this, like the first, used participants without motor-impairments. The third phase of the work then studied the applicability of the research findings thus far to groups of people with motor impairments, and in particular,the means of adapting the interaction techniques to individual abilities. In summary, the research has shown that collections of specialised gaze-based interaction techniques can be built as an effective means of completing the tasks in specific types of games and how these can be adapted to the differing abilities of individuals with motor impairments

MIDAS: Deep learning human action intention prediction from natural eye movement patterns

Eye movements have long been studied as a window into the attentional mechanisms of the human brain and made accessible as novelty style human-machine interfaces. However, not everything that we gaze upon, is something we want to interact with; this is known as the Midas Touch problem for gaze interfaces. To overcome the Midas Touch problem, present interfaces tend not to rely on natural gaze cues, but rather use dwell time or gaze gestures. Here we present an entirely data-driven approach to decode human intention for object manipulation tasks based solely on natural gaze cues. We run data collection experiments where 16 participants are given manipulation and inspection tasks to be performed on various objects on a table in front of them. The subjects' eye movements are recorded using wearable eye-trackers allowing the participants to freely move their head and gaze upon the scene. We use our Semantic Fovea, a convolutional neural network model to obtain the objects in the scene and their relation to gaze traces at every frame. We then evaluate the data and examine several ways to model the classification task for intention prediction. Our evaluation shows that intention prediction is not a naive result of the data, but rather relies on non-linear temporal processing of gaze cues. We model the task as a time series classification problem and design a bidirectional Long-Short-Term-Memory (LSTM) network architecture to decode intentions. Our results show that we can decode human intention of motion purely from natural gaze cues and object relative position, with $91.9\%$ accuracy. Our work demonstrates the feasibility of natural gaze as a Zero-UI interface for human-machine interaction, i.e., users will only need to act naturally, and do not need to interact with the interface itself or deviate from their natural eye movement patterns