Lee, YounghoPiumsomboon, ThammathipEns, BarrettLee, Gun A.Dey, ArindamBillinghurst, MarkTony Huang and Arindam Dey2017-11-212017-11-212017978-3-03868-052-9https://doi.org/10.2312/egve.20171364https://diglib.eg.org:443/handle/10.2312/egve20171364The rapid development of machine learning algorithms can be leveraged for potential software solutions in many domains including techniques for depth estimation of human eye gaze. In this paper, we propose an implicit and continuous data acquisition method for 3D gaze depth estimation for an optical see-Through head mounted display (OST-HMD) equipped with an eye tracker. Our method constantly monitoring and generating user gaze data for training our machine learning algorithm. The gaze data acquired through the eye-tracker include the inter-pupillary distance (IPD) and the gaze distance to the real and virtual target for each eye.Human centered computingMixed / augmented reality10.2312/egve.201713641-2