Aufsatz
Real-Time Eyeblink Detector and Eye State Classifier for Virtual Reality (VR) Headsets (Head-Mounted Displays, HMDs)
Abstract
The aim of the study is to develop a real-time eyeblink detection algorithm that can detect eyeblinks during the closing phase for a virtual reality headset (VR headset) and accordingly classify the eye’s current state (open or closed). The proposed method utilises analysis of a motion vector for detecting eyelid closure, and a Haar cascade classifier (HCC) for localising the eye in the captured frame. When the downward motion vector (DMV) is detected, a cross-correlation between the current region of interest (eye in the current frame) and a template image for an open eye is used for verifying eyelid closure. A finite state machine is used for decision making regarding eyeblink occurrence and tracking the eye state in a real-time video stream. The main contributions of this study are, first, the ability of the proposed algorithm to detect eyeblinks during the closing or the pause phases before the occurrence of the reopening phase of the eyeblink. Second, realising the proposed approach by implementing a valid real-time eyeblink detection sensor for a VR headset based on a real case scenario. The sensor is used in the ongoing study that we are conducting. The performance of the proposed method was 83.9% for accuracy, 91.8% for precision and 90.40% for the recall. The processing time for each frame took approximately 11 milliseconds. Additionally, we present a new dataset for non-frontal eye monitoring configuration for eyeblink tracking inside a VR headset. The data annotations are also included, such that the dataset can be used for method validation and performance evaluation in future studies.
Citation
In: Sensors 2019 / 5 (2019-03-05) , S. 1121 ; ISSN: 1424-8220Sponsorship
Gefördert durch den Publikationsfonds der Universität KasselCitation
@article{doi:10.17170/kobra-20190521506,
author={Alsaeedi, Nassr and Wloka, Dieter},
title={Real-Time Eyeblink Detector and Eye State Classifier for Virtual Reality (VR) Headsets (Head-Mounted Displays, HMDs)},
journal={Sensors},
year={2019}
}
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2019-05-21T12:44:49Z 2019-05-21T12:44:49Z 2019-03-05 doi:10.17170/kobra-20190521506 http://hdl.handle.net/123456789/11242 Gefördert durch den Publikationsfonds der Universität Kassel eng Urheberrechtlich geschützt https://rightsstatements.org/page/InC/1.0/ eyeblink real-time eye blink detection eye state classification head-mounted displays (HMD's) virtual reality motion vector analysis cross-correlation 004 Real-Time Eyeblink Detector and Eye State Classifier for Virtual Reality (VR) Headsets (Head-Mounted Displays, HMDs) Aufsatz The aim of the study is to develop a real-time eyeblink detection algorithm that can detect eyeblinks during the closing phase for a virtual reality headset (VR headset) and accordingly classify the eye’s current state (open or closed). The proposed method utilises analysis of a motion vector for detecting eyelid closure, and a Haar cascade classifier (HCC) for localising the eye in the captured frame. When the downward motion vector (DMV) is detected, a cross-correlation between the current region of interest (eye in the current frame) and a template image for an open eye is used for verifying eyelid closure. A finite state machine is used for decision making regarding eyeblink occurrence and tracking the eye state in a real-time video stream. The main contributions of this study are, first, the ability of the proposed algorithm to detect eyeblinks during the closing or the pause phases before the occurrence of the reopening phase of the eyeblink. Second, realising the proposed approach by implementing a valid real-time eyeblink detection sensor for a VR headset based on a real case scenario. The sensor is used in the ongoing study that we are conducting. The performance of the proposed method was 83.9% for accuracy, 91.8% for precision and 90.40% for the recall. The processing time for each frame took approximately 11 milliseconds. Additionally, we present a new dataset for non-frontal eye monitoring configuration for eyeblink tracking inside a VR headset. The data annotations are also included, such that the dataset can be used for method validation and performance evaluation in future studies. open access Alsaeedi, Nassr Wloka, Dieter doi:10.3390/s19051121 publishedVersion ISSN: 1424-8220 5 Sensors 1121 2019
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:Urheberrechtlich geschützt