CasualGaze: Towards Modeling and Recognizing Casual Gaze Behavior for Efficient Gaze-based Object Selection

TL;DR

CasualGaze introduces a gaze-based object selection method that enables users to select targets by casual glances, improving speed and accuracy over traditional active fixation methods.

Contribution

The paper presents a novel casual gaze-based selection technique with a new model and algorithms, supported by a comprehensive study of casual gaze behavior and an effective recognition system.

Findings

Significant improvement in recognition and selection speed.

Users preferred CasualGaze for efficiency and accuracy.

CasualGaze outperforms baseline techniques in usability tests.

Abstract

We present CasualGaze, a novel eye-gaze-based target selection technique to support natural and casual eye-gaze input. Unlike existing solutions that require users to keep the eye-gaze center on the target actively, CasualGaze allows users to glance at the target object to complete the selection simply. To understand casual gaze behavior, we studied the spatial distribution of casual gaze for different layouts and user behavior in a simulated real-world environment. Results revealed the impacts of object parameters, the speed and randomness features of casual gaze, and special gaze behavior patterns in "blurred areas". Based on the results, we devised CasualGaze algorithms, employing a bivariate Gaussian distribution model along with temporal compensation and voting algorithms for robust target prediction. Usability evaluation study showed significant improvements in recognition and selection speed for CasualGaze compared with two baseline techniques. Subjective ratings and comments further supported the preference for CasualGaze regarding efficiency, accuracy, and stability.