Closed-Eye Gaze Gestures: Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses

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Abstract

Gaze gestures bear potential for user input with mobile devices, especially smart glasses, due to being always available and hands-free. So far, gaze gesture recognition approaches have utilized open-eye movements only and disregarded closed-eye movements. This paper is a first investigation of the feasibility of detecting and recognizing closed-eye gaze gestures from close-up optical sources, e.g. eye-facing cameras embedded in smart glasses. We propose four different closed-eye gaze gesture protocols, which extend the alphabet of existing open-eye gaze gesture approaches. We further propose a methodology for detecting and extracting the corresponding closed-eye movements with full optical flow, time series processing, and machine learning. In the evaluation of the four protocols we find closed-eye gaze gestures to be detected 82.8%-91.6% of the time, and extracted gestures to be recognized correctly with an accuracy of 92.9%-99.2%.

Details

Original languageEnglish
Title of host publicationAdvances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings
EditorsIgnacio Rojas, Gonzalo Joya, Andreu Catala
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Work Conference on Artificial Neural Networks - Gran Canaria, Spain
Duration: 12 Jun 201914 Jun 2019
Conference number: 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11506 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Work Conference on Artificial Neural Networks
Abbreviated titleIWANN
CountrySpain
CityGran Canaria
Period12/06/201914/06/2019

    Research areas

  • Closed eyes, Gaze gestures, Machine learning, Mobile computing, Recognition, Smart glasses, Time series analysis

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