@conference{Somashekarappa.Sayeed.Howes,
author = "Vidya Somashekarappa and Asad Sayeed and Christine Howes",
abstract = "Gaze cues, which initiate an action or behaviour, are necessary for a responsive and intuitive interaction. Using gaze to signal intentions or request an action during
conversation is conventional. We propose a new approach to estimate gaze using a neural network architecture, while considering the dynamic patterns of real world gaze behaviour in natural interaction. The main goal is to provide foundation for robot/avatar to communicate with humans using natural multimodal-dialogue. Currently, robotic gaze systems are reactive in nature but our Gaze-Estimation framework can perform
unified gaze detection, gaze-object prediction and object-landmark heatmap in a single scene, which paves the way for a more proactive approach. We generated 2.4M gaze predictions of various types of gaze in a more natural setting (GHIGaze).
The predicted and categorised gaze data can be used to automate contextualized robotic gaze-tracking behaviour in interaction. We evaluate the performance on a manually annotated data set and a publicly available gaze-follow dataset. Compared to previously reported methods our model performs better with the closest angular error to that of a human annotator. As future work, we propose an implementable gaze
architecture for a social robot from Furhat robotics",
booktitle = "Proceedings of the 32nd IEEE International Conference on Robot and Human Interactive Communication",
title = "{N}eural {N}etwork {I}mplementation of {G}aze-{T}arget {P}rediction for {H}uman-{R}obot {I}nteraction",
url = "https://www.christinehowes.com/papers/Somashekarappa.etal_ROMAN_2023.pdf",
year = "2023",
}