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Evaluation of Gaze Depth Estimation using a Wearable Binocular Eye tracker and Machine Learning  

Shin, Choonsung (KETI)
Lee, Gun (University of South Australia)
Kim, Youngmin (KETI)
Hong, Jisoo (KETI)
Hong, Sung-Hee (KETI)
Kang, Hoonjong (KETI)
Lee, Youngho (Mokpo National University)
Publication Information
Journal of the Korea Computer Graphics Society / v.24, no.1, 2018 , pp. 19-26 More about this Journal
Abstract
In this paper, we propose a gaze depth estimation method based on a binocular eye tracker for virtual reality and augmented reality applications. The proposed gaze depth estimation method collects a wide range information of each eye from the eye tracker such as the pupil center, gaze direction, inter pupil distance. It then builds gaze estimation models using Multilayer perceptron which infers gaze depth with respect to the eye tracking information. Finally, we evaluated the gaze depth estimation method with 13 participants in two ways: the performance based on their individual models and the performance based on the generalized model. Through the evaluation, we found that the proposed estimation method recognized gaze depth with 90.1% accuracy for 13 individual participants and with 89.7% accuracy for including all participants.
Keywords
Gaze Depth; 3D gaze; Eye tracking; Virtual reality; Augmented reality;
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