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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Standard

Closed-Eye Gaze Gestures : Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses. / Findling, Rainhard Dieter; Nguyen, Le Ngu; Sigg, Stephan.

Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings. toim. / Ignacio Rojas; Gonzalo Joya; Andreu Catala. Springer Verlag, 2019. s. 322-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 11506 LNCS).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Harvard

Findling, RD, Nguyen, LN & Sigg, S 2019, Closed-Eye Gaze Gestures: Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses. julkaisussa I Rojas, G Joya & A Catala (toim), Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vuosikerta. 11506 LNCS, Springer Verlag, Sivut 322-334, Gran Canaria, Espanja, 12/06/2019. https://doi.org/10.1007/978-3-030-20521-8_27

APA

Findling, R. D., Nguyen, L. N., & Sigg, S. (2019). Closed-Eye Gaze Gestures: Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses. teoksessa I. Rojas, G. Joya, & A. Catala (Toimittajat), Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings (Sivut 322-334). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 11506 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-20521-8_27

Vancouver

Findling RD, Nguyen LN, Sigg S. Closed-Eye Gaze Gestures: Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses. julkaisussa Rojas I, Joya G, Catala A, toimittajat, Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings. Springer Verlag. 2019. s. 322-334. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-20521-8_27

Author

Findling, Rainhard Dieter ; Nguyen, Le Ngu ; Sigg, Stephan. / Closed-Eye Gaze Gestures : Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses. Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings. Toimittaja / Ignacio Rojas ; Gonzalo Joya ; Andreu Catala. Springer Verlag, 2019. Sivut 322-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex - Lataa

@inproceedings{fa1f73181b2549e7b8a19b201ac2aad1,
title = "Closed-Eye Gaze Gestures: Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses",
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{\%}.",
keywords = "Closed eyes, Gaze gestures, Machine learning, Mobile computing, Recognition, Smart glasses, Time series analysis",
author = "Findling, {Rainhard Dieter} and Nguyen, {Le Ngu} and Stephan Sigg",
year = "2019",
doi = "10.1007/978-3-030-20521-8_27",
language = "English",
isbn = "978-3-030-20520-1",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "322--334",
editor = "Ignacio Rojas and Gonzalo Joya and Andreu Catala",
booktitle = "Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings",

}

RIS - Lataa

TY - GEN

T1 - Closed-Eye Gaze Gestures

T2 - Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses

AU - Findling, Rainhard Dieter

AU - Nguyen, Le Ngu

AU - Sigg, Stephan

PY - 2019

Y1 - 2019

N2 - 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%.

AB - 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%.

KW - Closed eyes

KW - Gaze gestures

KW - Machine learning

KW - Mobile computing

KW - Recognition

KW - Smart glasses

KW - Time series analysis

UR - http://www.scopus.com/inward/record.url?scp=85067469791&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-20521-8_27

DO - 10.1007/978-3-030-20521-8_27

M3 - Conference contribution

SN - 978-3-030-20520-1

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 322

EP - 334

BT - Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings

A2 - Rojas, Ignacio

A2 - Joya, Gonzalo

A2 - Catala, Andreu

PB - Springer Verlag

ER -

ID: 35746268